Computer simulation of colloidal powder processing

In Paper I, packing of colloidal particles is simulated in three-dimensions. Colloidal particles in each cluster are condensed from space towards their center. Clusters are then moved toward the center of agglomerates. The particle rearrangement process during or after condensation is simulated by moving each particle into a more stable position. Particle clusters rearranged during packing have higher packing density and average coordination number than particle clusters rearranged after packing. The effect of particle rearrangement on the packing density in a cluster is comparable to the effect of cluster rearrangement in the cluster agglomeration;In Paper II, agglomeration of colloidal particles in a suspension is simulated using the concepts of interparticle potential fields. Colloid particles move during agglomeration in the direction which decreases the potential energies between particles. Simulated conditions are based on experimental results and on agglomeration theory. The effects of the various types of potential curves between colloidal particles were checked. It was found that short- and long-range fields contribute to the agglomeration process under typical default conditions. Strong short range attractive energy without a repulsive energy barrier makes small strong clusters with hard contacts between particles but weak short-range force with a large repulsive energy barrier makes big agglomerates with soft contacts between particles;In Paper III, settling of colloidal particles was studied with a three-dimensional computer simulation technique. Special attention was paid to the metastable state, rearrangement, and network stress build-up during or after colloidal particle settling. The settling density through the whole process was checked and compared with experimental results. There is a big difference in packing density between particles settled with and without rearrangement. Internal sediment stresses and fracture energies of the settled colloidal powders were calculated. Stress condensation in a simulated powder compact by internal sediment stresses shows good approximation to the network compression stage of powder settling experiments. In a vertical distribution of fracture energy several minima are observed along the height of the particle compact which may cause laminar cracks in subsequent drying processes. Abrupt changes in the fracture energy become smoother with particle rearrangement. Angular fracture energy becomes more isotropic with rearrangement. Low density regions were observed in the settled powder compact near the corner of a tilted box and this is mainly due to the bridging of particles

Health state classification of a spherical tank using a hybrid bag of features and K-nearest neighbor.

Feature analysis puts a great impact in determining the various health conditions of mechanical vessels. To achieve balance between traditional feature extraction and the automated feature selection process, a hybrid bag of features (HBoF) is designed for multiclass health state classification of spherical tanks in this paper. The proposed HBoF is composed of (a) the acoustic emission (AE) features and (b) the time and frequency based statistical features. A wrapper-based feature chooser algorithm, Boruta, is utilized to extract the most intrinsic feature set from HBoF. The selective feature matrix is passed to the multi-class k-nearest neighbor (k-NN) algorithm to differentiate among normal condition (NC) and two faulty conditions (FC1 and FC2). Experimental results demonstrate that the proposed methodology generates an average 99.7% accuracy for all working conditions. Moreover, it outperforms the existing state-of-art works by achieving at least 19.4%

A Hybrid Technique for Medical Image Segmentation

Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR) image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images

New and Renewable Energy Policies of Jeju Island in Korea

노트 : World Renewable Energy congress 2011 – Sweden 8-13 May 2011, Linkoping, Swede

A novel framework for centrifugal pump fault diagnosis by selecting fault characteristic coefficients of Walsh transform and cosine linear discriminant analysis.

In this paper, we propose a three-stage lightweight framework for centrifugal pump fault diagnosis. First, the centrifugal pump vibration signatures are fast transformed using a Walsh transform, and Walsh spectra are obtained. To overcome the hefty noise produced by macro-structural vibration, the proposed method selects the fault characteristic coefficients of the Walsh spectrum. In the second stage, statistical features in the time and Walsh spectrum domain are extracted from the selected fault characteristic coefficients of the Walsh transform. These extracted raw statistical features result in a hybrid high-dimensional space. Not all these extracted features help illustrate the condition of the centrifugal pump. To overcome this issue, novel cosine linear discriminant analysis is introduced in the third stage. Cosine linear discriminant analysis is a dimensionality reduction technique which selects similar interclass features and adds them to the illustrative feature pool, which contains key discriminant features that represent the condition of the centrifugal pump. To achieve maximum between-class separation, linear discriminant analysis is then applied to the illustrative feature pool. This combination of illustrative feature pool creation and linear discriminant analysis forms the proposed application of cosine linear discriminant analysis. The reduced discriminant feature set obtained from cosine linear discriminant analysis is then given as an input to the K-nearest neighbor classifier for classification. The classification results obtained from the proposed method outperform the previously presented state-of-the-art methods in terms of fault classification accuracy

Residual Frequency Offset Compensation Using the Approximate SAGE Algorithm for OFDM System

In this letter, we propose an iterative detection scheme in the presence of residual frequency offset (RFO) for orthogonal frequency-division multiplexing systems using an approximate application of the space-alternating generalized expectation-maximization (SAGE) algorithm. In the proposed scheme, the expectation step intends to divide the received signal into the desired signal and the interference signal. In the maximization step, the desired signal is used to estimate required parameters (i.e., RFO, data symbols, and channel state information) sequentially. Simulation results present that the proposed scheme shows almost ideal performance as long as the normalized RFO value is within 0.2.This work was supported in part by the University IT Research Center Project and in part by the Brain Korea 21 Project