Deflagration experiments of trace explosive were presented; a kind of sensitive silicon beam for detecting radiation in the deflagration of trace explosive was designed. The silicon beam had a heating and a thermal re...
详细信息
Deflagration experiments of trace explosive were presented; a kind of sensitive silicon beam for detecting radiation in the deflagration of trace explosive was designed. The silicon beam had a heating and a thermal resistors on it, used as stimulating and detecting elements respectively. thermal-electric simulation of the silicon beam was carried out by finite element analysis software ANSYS, and some silicon beams of this kind have already been fabricating now. These works provide basis for developing a new device for detecting trace explosive.
This paper presents a comprehensive overview of the current state of research in the area of Networked control systems (NCSs) first. Then two modeling and control methods are introduced for NCSs in details. The first ...
详细信息
This paper presents a comprehensive overview of the current state of research in the area of Networked control systems (NCSs) first. Then two modeling and control methods are introduced for NCSs in details. The first one is a stochastic control method, which investigates the H{sub}∞ control problem for NCSs with random network-induced delay. The second one is a switch control method, which focuses on solving the stabilization problem for NCSs in discrete-time domain, where both network-induced delay and packet dropout are taken into account. Illustrative examples are given to demonstrate the effectiveness of the proposed approaches. Finally, this paper concludes with the discussion of possible future development of NCSs from a control perspective.
The contrast function remains to be an open problem in blind source separation （BSS） when the number of source signals is unknown and/or dynamically changed. The paper studies this problem and proves that the mutual...
详细信息
The contrast function remains to be an open problem in blind source separation （BSS） when the number of source signals is unknown and/or dynamically changed. The paper studies this problem and proves that the mutual information is still the contrast function for BSS if the mixing matrix is of full column rank. The mutual information reaches its minimum at the separation points, where the random outputs of the BSS system are the scaled and permuted source signals, while the others are zero outputs. Using the property that the transpose of the mixing matrix and a matrix composed by m observed signals have the indentical null space with probability one, a practical method, which can detect the unknown number of source signals n, ulteriorly traces the dynamical change of the sources number with a few of data, is proposed. The effectiveness of the proposed theorey and the developed novel algorithm is verified by adaptive BSS simulations with unknown and dynamically changing number of source signals.
A novel semi-supervised learning approach is proposed based on a linear neighborhood model, which assumes that each data point can be linearly reconstructed from its neighborhood. Our algorithm, named Linear Neighborh...
详细信息
ISBN:
(纸本)1595933832
A novel semi-supervised learning approach is proposed based on a linear neighborhood model, which assumes that each data point can be linearly reconstructed from its neighborhood. Our algorithm, named Linear Neighborhood Propagation (LNP), can propagate the labels from the labeled points to the whole dataset using these linear neighborhoods with sufficient smoothness. We also derive an easy way to extend LNP to out-of-sample data. Promising experimental results are presented for synthetic data, digit and text classification tasks.
A novel semi-supervised learning approach is proposed based on a linear neighborhood model, which assumes that each data point can be linearly reconstructed from its neighborhood. Our algorithm, named Linear Neighborh...
详细信息
ISBN:
(纸本)1595933832
A novel semi-supervised learning approach is proposed based on a linear neighborhood model, which assumes that each data point can be linearly reconstructed from its neighborhood. Our algorithm, named Linear Neighborhood Propagation (LNP), can propagate the labels from the labeled points to the whole dataset using these linear neighborhoods with sufficient smoothness. We also derive an easy way to extend LNP to out-of-sample data. Promising experimental results are presented for synthetic data, digit and text classification tasks.
This paper introduces a discriminative method for semi-automated segmentation of the tumorous tissues. Due to the large data of 3D MR brain images and the blurry boundary of the pathological tissues, the segmentation ...
详细信息
In this paper we present a novel parameter learning and identification method of virtual garment. We innovate in the ordinary parameter identification process and introduce the fabric data (Kawabata Evaluation system ...
详细信息
In this paper, we accomplish matching score level fusion of multi-biometrics. In order to solve the incomparability among different classifiers' outputs, Adaptive Confidence Transform (ACT) is introduced to conver...
详细信息
This paper considers the problem of perturbation analysis for the QR factorization of a special architecture called unitary-symmetric matrix. The perturbation bounds of the triangular factor and orthogonal factor in t...
详细信息
This paper considers the problem of perturbation analysis for the QR factorization of a special architecture called unitary-symmetric matrix. The perturbation bounds of the triangular factor and orthogonal factor in the QR factorization of unitary-symmetric matrix arc derived, together with the perturbation bound correspondences between the unitary-symmetric matrix and its mother matrix highlighted.
In this paper, we address the general problem of learning from both labeled and unlabeled data. Based on the reasonable assumption that the label of each data can be linearly reconstructed from its neighbors' labe...
详细信息
暂无评论