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This paper proposes a new modeling method for induction motor (IM) field oriented control (FOC) system based on co-simulation of Maxwell, Simplerer and Simulink. This method uses Ansys/Rmxprt to calculate the initial value and optimize the performance of motor, and puts motor model into Maxwell 2D to make static and transient analysis by finite element method. The circuits of the inverter are built...
In this paper, we consider the problem of unsupervised feature selection. Recently, spectral feature selection algorithms, which leverage both graph Laplacian and spectral regression, have received increasing attention. However, existing spectral feature selection algorithms suffer from two major problems: 1) since the graph Laplacian is constructed from the original feature space, noisy and irrelevant...
The k-Nearest Neighbor (k-NN) graphs are widely used in data mining and machine learning. How to construct a high quality k-NN graph for generic similarity measures efficiently is crucial for many applications. In this paper, we propose a new approach to effectively and efficiently construct an approximate k-NN graph. Our framework is as follows: (1) generate a random k-NN graph approximation, Gf,...
In our lower limb rehabilitation system, the surface electromyography (sEMG) signal is adopted as the signal of human-machine interface. In order to be able to control rehabilitation robot in real time, the paper proposes a new and real-time sEMG signal segmentation method P&WND that is accomplished by way of the analysis of peak information and adding non-equidistant window function. The...
IP geolocation services are playing an increasingly important role in Web applications. Landmark-based IP geolocation can gain better IP geolocation performance. However, the accuracy of landmarks is still an urgent issue to address. The paper proposes a filtering landmark mechanism to guarantee the accuracy of landmarks. First, a Ping-Delay Coarse Filtering Landmark algorithm (PCFL) is proposed to...
Traffic classification plays an important role in many short to medium term network management tasks and in long term network dimensioning/planning. In recent years a number of traffic classifiers have been proposed, in particular classifiers based on machine learning techniques exhibit high levels of accuracy. However, in practice, even if classifiers can be accurately trained at a given time, their...
Many existing machine learning based traffic classifiers require the first five packets in traffic flows to perform traffic classification. In this work, we investigate the flexibility of using arbitrary sets of packets to train traffic classifiers. Such classifiers could be used as auxiliary classifiers that would function in cases where some packets in flows are unavailable, possibly due to packet...
In robot soccer competition, it is important that the robot can identify and approach the ball quickly and accurately. This paper presents a fuzzy control method which could improve the accuracy and speed to recognize ball. The conventional robot control consists of methods for path generation and following. When a robot moves away the desired track, it must return immediately, and while doing so,...
Human robot interaction is an emerging area of research, where human understandable robotic representations can play a major role. Knowledge of semantic labels of places can be used to effectively communicate with people and to develop efficient navigation solutions in complex environments. In this paper, we propose a new approach that enables a robot to learn and classify observations in an indoor...
We propose a clustering algorithm based on a structural prior based Local Factor Analysis (spLFA) model under the Bayesian Ying-Yang harmony learning, which automatically determines the hidden dimensionalities during parameter learning, reduces the number of free parameters by projecting the mean vectors onto a low dimensional manifold, imposes the sparseness by a Normal-Jeffreys prior. Experiments...
Failure detection is a key technology to implement a high reliable system. It is usually based on overtime mechanism to determine whether a process is failure or not. With the development of network, old failure detectors without adaptive mechanism can not meet the requirements of QoS of application all the time. Adaptive failure detection requires that the failure detectors can dynamically adjust...
The number of variables used for credit scoring can be quite large, and selecting the most relevant variables becomes an important topic. In this paper, we use gradient learning method for variable selection in credit scoring. The original method in the literature does not work on credit datasets because of the large sample size. To conquer this, we modify the algorithm by resampling data and voting...
Bankruptcy prediction has been one of the most challenging tasks and a major research topic in accounting and finance. In this paper, bagging ensemble, a popular technique in the machine learning community, is proposed to improve the prediction performance of artificial neural networks in bankruptcy prediction analysis. The experiments conducted on the public dataset show that the proposed approach...
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