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Bearings are one of the most omnipresent and vulnerable components in rotary machinery such as motors, generators, gearboxes, or wind turbines. The consequences of a bearing fault range from production losses to critical safety issues. To mitigate these consequences condition based maintenance is gaining momentum. This is based on a variety of fault diagnosis techniques where fuzzy clustering plays...
Selecting an adequate machine learning model, e.g. for feature selection or classification, is a very important task in developing machine learning applications. In order to perform an adequate selection, statistic tests are introduced by several approaches but some of them are hard to reproduce in different case studies due to the lack of a systematic application procedure. This work presents a methodological...
It is a pervasive problem to accurately estimate the frequency of sinusoids contaminated by random noise, which has existed in many signal processing areas, including the application in mechanical fault diagnosis and prognostics. The interpolation discrete Fourier transform (DFT) method, employed in frequency domain, is one of the most well studied frequency estimation methods. In this paper, a comparison...
Nowadays, the available data to describe real world problems grows in considerable manner, due to the amount of measurable characteristics (features) that can be collected. Machine learning techniques are widely used to extract valuable knowledge from data, but their performance might decrease when the proper features are not selected. Feature selection is introduced to search relations to disclose...
Aviation network is the typical information network, where the airports can be regarded as nodes, and the airlines between airports can be regarded as links. But with the constraints with the Degree-Centrality, Closeness-Centrality and the Centrality between nodes, traditional researches on the influence of the information network developed slowly. Thus, we do some experiments on the traditional methods...
Social network influence maximization problem aims to design algorithms that can maximize the scope of the nodes affected by the specified nodes set. This paper studies and improves existing algorithms by the introduction of hidden influence and floating influence. Experiments show that the HGA algorithm works more effectively.
In cognitive radio networks, the available spectrum holes are usually discontinuous. Hence, it is hard to exploit these spectrum holes because the bandwidth of an individual one may not be able to satisfy the wide bandwidth requirement imposed by secondary users (SUs). Spectrum aggregation (SA) enables SUs to integrate several spectrum holes into one channel with wide bandwidth which may support high...
In cognitive radio networks, the available vacant frequency bands, referred to as spectrum holes, are usually discontinuous. Hence, it is hard to exploit these vacant frequency bands since the bandwidth of single one may not be able to meet the wide bandwidth requirement of secondary users (SUs). Spectrum aggregation (SA) enables SUs to access several discontinuous spectrum holes simultaneously to...
With the increased requirements on the precision of image products, the parallel imaging of high resolution SAR data has been receiving much more concerns nowadays. However, many existed parallel algorithms haven't given adequate attentions to both the architecture of a certain parallel computer and the computational features (e.g. matrix transposition) of a SAR imaging algorithm, which couldn't reach...
In traditional Gene Expression Programming (GEP), each chromosome is expressed and evaluated on the Expression Tree (ET). The ET-based expression and evaluation are computationally expensive and the intelligibility of the chromosome is low. In this paper, a highly efficient algorithm, Reduced-GEP, is proposed to solve these problems. First, the chromosome is reduced by Reduced-GEP. Second, chromosomes...
Gene expression programming (GEP) is a new member of evolutionary computation family, and is successful in symbolic regression and function finding in the field of data mining. However, GEP is difficult to find power functions with high ranks. To tackle this problem, this study proposes a novel GEP algorithm named HDN-GEP. The main contributions include: (1) a new structure named HDN (high density...
Monitoring cluster evolution in data streams is a major research topic in data streams mining. Previous clustering methods for evolving data streams focus on global clustering result. It may lose critical information about individual cluster. This paper introduces some basic movements of evolution of an individual cluster. Based on the measurement of the movements, a novel algorithm called MovStream...
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