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In the literature, a number of methods have been proposed for semi-supervised learning. Recently, graph-based methods of semi-supervised learning have become popular because of their capability of handling large amounts of unlabeled data. However, the existing graph based semi-supervised learning algorithms do not optimize the process of selecting better labeled data. We have developed a new selective...
In many laser processing applications, galvanometer scanners are integrated to a larger system that creates external disturbances to the actual laser-material interaction. To reject such disturbance with feedback-based control schemes, the sampling of the output needs to be fast enough to capture all major frequency components of the disturbance. In some applications, however, the sensor's sampling...
A RBF-based neural network adaptive particle swarm optimization algorithm is proposed in this paper. In this algorithm, code at particle position adopts quantum bit to realize. The paper adopt particle flight path information to dynamically update the status of quantum bit and introduces quantum non-gate to realize mutation operation so as to avoid local optimization. Then, it is used to train neural...
This paper presents an online self-tuning Smith Predictor for the First Order Plus Dead Time Model (FOPDT). It can tune time delay through oscillated input and output. Realtime phase difference detection is used to obtain the phase difference between the input and the output. An online tuner is used to minimize the phase difference. Once the phase difference is minimized, the exact time delay is completely...
In this paper, a networked-based rehabilitation system is introduced for lower-extremity tele-rehabilitation. In order to enable high-level motion planning of the rehabilitation robot in real-time for enhanced safety and appropriate human-robot interactions, a time series model is proposed to capture the kinematics of knee joint rotations. A major challenge in such a system is that measurement data...
A model based on partial mutual information and fixed size least squares support vector machine is proposed, which used to deal with multivariable and large scale regression problem for prediction of saltwater intrusion,. The partial mutual information is used to select the inputs and its lag orders. A sparse approximation of input samples is done with an active selection of support vectors based...
Applying SRGMs (Software Reliability Growth Models) to real projects is a major concern in software reliability. Sometimes, it is hard to decide the best model for a specific project. Researchers have made a first step on solving this problem by combination, but the effect was limited in accuracy and adaptability. Aiming to improve the usability of the SRGMs, we propose a neural network based combination...
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