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The correlation between pyrolysis conditions and fuel production has been extensively studied. Terahertz parameters, instead of thermal kinetic parameters, were investigated to reveal such interior correlation in this work. Different ventilating rate (V), heating rate (β) and final temperature (T) were controlled in several pyrolysis experiments of oil shale to prepare semicoke with varied amount...
In this paper, we extend the nearest convex hull classifier to Symmetric Positive Definite (SPD) manifolds. SPD manifold features have been shown to have excellent performance in various image/video classification tasks. Unfortunately, SPD manifolds naturally possess non-Euclidean geometry, so existing Euclidean machineries such as the nearest convex hull classifier cannot be used directly. To that...
This paper examines the capacity of a time division duplex (TDD) multiple input single output (MISO) beamforming system with channel estimation error and delay over the time varying fading channel. In TDD system, the base station estimates the channel state information (CSI) at transmitter based on uplink pilots and then uses it to generate the beamforming vector in the downlink transmission. Because...
An optimal scheme on uplink pilot time interval (UPTI) to maximize average post-processing SNR (signal to noise ratio) is proposed in order to overcome the impact of channel estimation error and delay on a time division duplex (TDD) multiple input single output (MISO) beamforming system. In TDD system, the base station estimates the channel state information (CSI) at transmitter based on uplink pilots...
We consider the problem of fuzzy community detection in networks, which complements the concept of overlapping community structure. Using the optimization method to approximate network feature matrix is an important approach for conventional fuzzy community detection. In order to retain valuable physical meaning of the approximation, we discard redundant constraints in the process of approximation...
Support vector machines (SVMs) have been dominant learning techniques for more than ten years, and mostly applied to supervised learning problems. These years two-class unsupervised and semi-supervised classification algorithms based on bounded C-SVMs, bounded j/-SVMs and Lagrangian SVMs (LSVMs) respectively, which are relaxed to semi-definite programming (SDP), get good classification results. These...
Support Vector Machines (SVMs) have been dominant learning techniques for more than ten years, and mostly applied to supervised learning problems. These years two-class unsupervised and semi-supervised classification algorithms based on Bounded C-SVMs, Bounded n-SVMs and Lagrangian SVMs (LSVMs) respectively, which are relaxed to Semi-definite Programming (SDP), get good classification results. These...
Support vector machines (SVMs) have been dominant learning techniques for more than ten years, and mostly applied to supervised learning problems. These years two-class unsupervised and semi-supervised classification algorithms based on bounded C-SVMs, bounded ??-SVMs and Lagrangian SVMs (LSVMs) respectively, which are relaxed to semi-definite programming (SDP), get good classification results. These...
Support vector machines (SVMs) have been dominant learning techniques for almost ten years, and mostly applied to supervised learning problems. Recently nice results are obtained by two-class unsupervised and semi-supervised classification algorithms where the optimization problems based on bounded C-SVMs, bounded v-SVMs and Lagrangian SVMs respectively are relaxed to semi-definite programming (SDP)...
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