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The load curve presents certain randomness for reasons such as human social activities, the load of electric vehicle charging and discharging and so on, which covers up the regularity of load sequence. This paper proposes an approach which can restore the original feature of the loads. In this approach, firstly, the bad data is excluded; Secondly, the characteristics of the time series are extracted...
Interest in traffic classification has dramatically grown in the past few years in both industry and academia. As more and more applications are encrypting the payloads and not to use well-known ports, traditional traffic classification methods such as transport-layer protocol ports based ones can not accurately and efficiently deal with these applications. In this paper we investigate the problem...
We present a study of designing compact multiple-prototype based classifiers for rotation-free recognition of online handwritten Chinese characters. Several versions of Rprop algorithms are adopted to optimize a sample-separation-margin based minimum classification error objective function. Split vector quantization technique is used to compress classifier parameters and a fast-match tree is used...
Cellular network based context-aware applications and location based services (LBSs) have drawn significant attention in both research and industry for many years. A key aspect that influences the quality of context-aware applications and LBSs is the localization accuracy of the mobile terminal (MT). The empirical location estimation method, also known as the fingerprint method, is a popular location...
This Internet traffic classification using Machine Learning is an emerging research field since 1990's, and now it is widely used in numerous network activities. The classification technique focuses on modeling attributes and features of data flows to accomplish the identification of applications. In the paper we design and implement the classification model based on header-derived flow statistical...
This paper presents a new method to identify languages. A LVQ (learning vector quantization) network aimed at language identification is introduced. The presence of particular characters, words and the statistical information of word lengths are used as a feature vector. The new classification technique is faster than the conventional N-gram based classification approach, but it performs similarly...
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