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Geometry-based stochastic model (GBSM) of multiple input multiple output (MIMO) channel describes the channel impulse response (CIR) in the sense of rays and clusters, which obey the empirical distributions. Thus, the correlation between MIMO sub-channels is not explicitly defined, which makes it difficult for GBSM to predict channel capacity accurately. Facing the increased antenna number of massive...
The present work proposes a new technique for bearing fault classification that combines time-frequency analysis with image processing. This technique uses vibration signals from bearing housings to detect bearing conditions and classify the faults. The signals are decomposed by means of Empirical Mode Decomposition (EMD) and Principal Components Analysis (PCA) in order to obtain the principal components...
For big, high-dimensional dense features, it is important to learn compact binary codes or compress them for greater memory efficiency. This paper proposes a Binarized Multilinear PCA (BMP) method for this problem with Free-Form Reshaping (FFR) of such features to higher-order tensors, lifting the structure-modelling restriction in traditional tensor models. The reshaped tensors are transformed to...
Until now, the canonical correlation analysis (CCA)-based method has been most widely applied to steady-state visual evoked potential (SSVEP). Artificial sine-cosine signals are used as the original references in the CCA method, which could hardly reflect the real SSVEP features buried in electroencephalogram (EEG). In this study, we use principal component analysis (PCA) to extract EEG features multivariate...
Steering control for vehicle lane keeping has attracted great attentions from both automotive industries and vehicle control researchers. Traditionally commonly used linear control model can not adequately represent the intermitted pulse-like qualities in real-world steering angle measurements. Therefore, based on an alternative ‘pulse control model’ of steering control for vehicle lane keeping, steering...
Since the changes of raw material properties, external environment and other conditions, during practical industrial processes, multiple stable operation modes may arise, and between any two stable modes may undergo slowly changing transition modes. The existing multimode process monitoring methods haven't monitored dynamic characteristics of the transition modes efficiently. This paper adopts differential...
The paper presents a Variational Bayesian (VB) method to allow a Gaussian Mixture Model (GMM) to be clustered automatically with its mixture components in order to facilitate the discrimination of what can be regarded as steady-state and transient machine operation. The determination of whether a unit is considered to be in steady-state, or subject to external transients is an important pre-processing...
The performances of light-weight and the anti-damage are a pair of conflicting optimization goals. Basing on the damage data of explosion simulation, it studies multi-objective optimization of cantilever box girder about lightweight and anti-damage. Firstly, the method of PCA is introduced to mine the damage data of simulation experiments. Secondly -- the regression function about the damage of cantilever...
The paper presents two approaches for sensor fault detection and discrimination within a group, in real-time. In the first approach, the concept of y-indices is proposed through use of a transpose formulation of the data matrices traditionally used in Principal Component Analysis (PCA). The proposed formulation is introduced to measure the differences between sensor reading datasets in the ‘sensor...
DNA of eukaryotic cells is organized into repeating nucleosomes. As nucleosomes significantly limit the accessibility of their DNA to transcription factors and other DNA-binding proteins, their positions play an essential role in regulations of gene activities [4, 8]. Recent genome wide experiments indicate that DNA sequences themselves strongly influence nucleosome positioning by enhancing or reducing...
This paper proposes a class of principal component analysis (PCA) learning algorithms with constant learning rates. It will prove via deterministic discrete time (DDT) method that these PCA learning algorithms are globally convergent.
In order to meet real-time requirements of strip surface defect detection, the extracted 41 original features of strip surface defects are reduced-dimensionally optimized through the principal component analysis. As the study samples of improved BP neural network, the 10 integrated features are used to train and test network. The trained network model is saved for on-line recognition and classification...
Nowadays China has speeded up urbanization, urban land use occurred in areas of significant change. In order to obtain land cover information speedily and correctly, many methods from data mining are used to classify the remote sensing image. In recent years, using decision trees (DTs) to classify remotely sensed data has increased, due to the algorithm running fast and making no statistical assumptions...
This paper presents a novel method for generating varied, realistic geometric face models by synthesizing facial features according to anthropometric parameters. Our method takes as examples scanned 3D face models in order to exploit the parameter-to-geometry correlations that are presented in the real faces. A two-step model fitting approach is used to establish correspondences among scanned models...
A core competence evaluation index system of high-tech enterprise is established according to the characteristics of high-tech enterprise and actual conditions of S City. Principal component analysis is applied to evaluate the core competence of 60 enterprises from three leading industry of the city. By associating the four principal components with three competence levels (including core competence,...
This paper presents a novel data-driven method for creating varied realistic face models by synthesizing a set of facial features according to intuitive high-level control parameters. Our method takes as examples 3D face scans in order to exploit the variations presented in the real faces of individuals. We use an automatic model fitting approach for the 3D registration problem. Once we have a common...
This paper presents an automatic runtime system for generating varied realistic geometric models of human faces based on morphing of facial features. Our method takes as examples 3D scanned human face models in order to exploit the parameter-to-geometry correlations that are presented in the real faces. In order to establish correspondence among models, we use a two-step model fitting approach to...
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