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Along with the rapidly development of the information retrieval and web technology, web entity retrieval has become a new popular way for getting specific information, such as looking for a book or a movie. Like document retrieval, generally there are too many results returned for a query, so ranking is still a necessary step during the entity retrieval process. This paper will focus on the ranking...
Artificial neural network (ANN) is an important part of artificial intelligence, it has been widely used in remote sensing classification research field. Wetlands remote sensing classification based on ANN is difficult, because of the complex feature of wetlands areas. The purity of training samples for remote sensing image supervised classification is difficult to guarantee that will affect the classification...
The objective of this paper is to propose a new system for fault diagnosis of train bearings using PCA and ACO. On the base of the analysis of time and frequency domain statistical features extracted from the vibration signals collected from the bearings, twenty features which were the most sensitive to different working states were chosen as the object of follow-on process. After zero-average and...
Advances in object detection have made it possible to collect large databases of certain objects. In this paper we exploit these datasets for within-object classification. For example, we classify gender in face images, pose in pedestrian images and phenotype in cell images. Previous work has mainly targeted the above tasks individually using object specific representations. Here, we propose a general...
Bandwidth efficiency is a critical concern in wireless communications. To fully utilize the available bandwidth, this paper adopts the superimposed training (ST) scheme in orthogonal frequency division multiplexing (OFDM) systems without using cyclic prefix (CP) and guard interval (GI). If the pilot pattern is fixed, it is shown that the performance of the channel estimation using the ST scheme is...
The nondestructive optical coherence tomography measurement can show the shell-nucleus features, quantify the nacre thickness and thus has the potential to identify or grade the pearls [1]. However, the automated thickness measurement of nacreous layer based on OCT has not been reported. In this article, an automated approach is first time proposed to measure the thickness of nacreous layer using...
The marine diesel engine is a complex system, which has the important function to guarantee the marine security. There is strong coupling relationship among the mapping process of fault diagnosis. An approach of intelligent fault diagnosis based on fuzzy neural network optimized and trained by the genetic algorithm (GA) was proposed in this paper for this system. The structure and the parameters of...
The marine diesel engine is a complex system. Its mapping process of fault diagnosis has multi-fault attributes, which means input and output of fault pattern attribute are the multi-mapping relations. An approach of intelligent fault diagnosis using fuzzy neural networks and genetic algorithms to optimize and train is studied in this paper for this system. The structure and the model of intelligent...
Support vector machine (SVM) has been widely studied and shown success in many application fields. However, the performance of SVM drops significantly when it is applied to the problem of learning from imbalanced data sets in which negative instances greatly outnumber the positive instances. This paper analyzes the intrinsic factors behind this failure and proposes a suitable re-sampling method. We...
A new algorithm named wavelet transform weighted modular PCA is proposed for face recognition. Firstly, the training images and the testing image are preprocessed with wavelet transform and the LL band and the LH/HL average band are divided into sub-images with the same size. Secondly, the prospective classify contribution of each sub-model of the two bands are computed. Thirdly, each sub-image of...
Multicategory support vector machines (MC-SVM) are powerful classification systems with excellent performance in a variety of biological classification problems. However, the process of generating models in traditional multicategory support vector machines is very time-consuming, especially for large datasets. In this paper, parallel multicategory support vector machines (PMC-SVM) have been developed...
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