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Quadrotor helicopter is an increasingly popular rotorcraft platform for unmanned aerial vehicle (UAV) study. Development of a mathematical model with accurate model parameters for quadrotor is extremely beneficial to control the system. But accurate measurement of numerous vehicle parameters would be especially challenging. In this paper, a system identification method for the quadrotor model parameter...
Image registration is one of the crucial steps in the analysis of remotely sensed data. A new acquired image must be transformed, using image registration techniques, to match the orientation and scale of the related reference images. Image registration requires intensive computational effort not only because of its computational complexity, but also due to the continuous increase in image resolution...
The spatial resolution of hyper-spectral remote sensing image is not enough to describe the distribution of land cover classes in the mixed pixel, sub-pixel mapping (SPM) is a promising way to predict the location of end-member at the sub-pixel level, based on the fraction images which were generated by spectral un-mixing. In this paper, a novel method was proposed to realize SPM. The proposed method...
When it comes to complex biological problems the use of conventional computation techniques has shown not to be the best approach. With the aim of selecting small sets of genes, that have strong predictive correlations with a disease, the Genetic Algorithms (GAs) are being increasingly used. In this paper, we propose a hybrid approach, using methods of feature selection and a classifier based on GA...
The issue of studying the effect of fixing the length of the selected feature subsets using ant colony optimization (ACO) has not yet been studied. This paper addresses this concern by demonstrating four points that are: 1) determining the optimal feature subset, 2) determining the length of the subsets in ACO for subset selection problems, 3) different stopping criteria when solving feature selection...
The traditional BP neural network training method processes the training dataset serially on one machine, so the efficiency is quite low. The massive data that need to be explored brings great challenge for BP neural network. The traditional serial training method of BP neural network will encounter many problems, such as costing too much time and insufficient memory to finish the training process...
In view of the classification favors seriously to the most kinds when we use the traditional sorter to classify the imbalanced data set and the errors of classification of minority kind is big, A new minority kind of sample sampling method based on genetic algorithm and K-means cluster is proposed. First the method clusters and groups the minority kind of sample through K-means algorithm, then gains...
The development of World Wide Web (WWW) a little more than a decade ago has caused an information explosion that needs an Intelligent Web (IW) for users to easily control their information and commercial needs. Therefore, engineering schools have offered a variety of IW courses to cultivate hands-on experience and training for industrial systems. In this study, Intelligent Teaching Models for STEM...
According to the BP neural network fault line when the input data amount is large, its structure is complex, convergence slowly, and easy to fall into the local optimal shortcomings, we will put fuzzy rough sets and the genetic algorithm to optimize the method of neural network into one-phase ground fault distribution network in line. We obtained the line of zero sequence current signals through the...
This paper presents a novel experimental design for greatly improving the calibration accuracy of the acceleration-insensitive and the acceleration-sensitive biases of the dynamically tuned gyroscopes. A novel calibration procedure based on D-optimality criteria and real code genetic algorithm (RCGA) is established. In order to reduce experiment cost, the D-optimality criteria is constructed with...
Consumer credit prediction is considered as an important issue in the credit industry. The credit department often makes decision which depends on intuitive experience with large risk. This study proposed a new model that hybridized the support vector machine (SVM) and particle swarm optimization (PSO) to evaluate the new consumer's credit score. The hybrid model simultaneously optimizes the SVM kernel...
In this paper we propose FM-PGA, a MapReduce-based hybrid of FM-test and Parallel Genetic Algorithm (PGA), to analyze gene micro array data on cloud computing platform. We investigate the performance of FM-PGA on real-world micro array data and compare it with FM-GA and MapReduce PGA. The experimental results confirm that the genes selected by FM-PGA achieve comparable classification accuracy while...
In the applications of wireless sensor networks, the effects of data reflection largely depend on the accuracy of localization of the information collection node. Therefore, the position information of nodes is very important. Without it, events can not be sensed and measured, and value of application will be lost. To improve the location accuracy of nodes in wireless sensor networks, the paper puts...
Policy of national student loans accelerates the reform of higher education in China and the process of market mechanism of talents training in a very great degree, and provides the important guarantee for the poor college students. However, at present, high default rate makes commercial bank which provides student loans bear the risk of bad debt, and affects the policy of national student loan to...
This paper presents a method to determine music-motion correspondence.For specific type of dance and music,system extract low-level features,calculate correspondence ,then select muisc-motion correspondence using genetic algorithms,and get a correspondence satisfing match accuracy and operation speed in the end.The experimental results indicate that system fully express the changes between music and...
Many optimization problems in the scientific research and engineering practice can be modeled as multi-objective optimization problems. Effective algorithms for them is of not only important in scientific research, but also valuable in applications. In this paper, a new genetic algorithm for multi-objective optimization problems based on uniform design called BUMOGA is proposed combined with uniform...
When solving an optimal problem, different encoding method has an important effect on the performance of multi-objective genetic algorithm. This paper breaks the traditional binary coding ideas, introduces a new dominant-recessive diploid codes which applied in the MOGA. we analyses the impact on solution space by the binary multi-objective genetic algorithm and dominant-recessive diploid codes multi-objective...
Brain-computer interface (BCI) is a specific Human-Computer interface in which the brain wave is employed as the carrier of control information. The ultimate goal of BCI is to build a direct communication pathway between human brain and external environment that does not depend on the limb mobility and language. In this paper, we carry out the experiment about the left or right hand motor imagery,...
To solve the problem of slow convergence speed of the standard genetic algorithm (SGA), the strategy of adaptively changing the search area is used to reduce the search area progressively in this paper. The tactics of concerted evolution among multiple populations is proposed aimed at the deficiency of easily plunging into a local optimal solution of SGA. Distant hybridization strategy and a new method...
In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from high-dimensional data streams. We conduct a case study of SPOT in this paper by deploying it on 1999 KDD Intrusion Detection application. Innovative approaches for training...
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