Browsing by Author "Pan, Erte"
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Item Big Data Analysis of Complex Networks Using Machine Learning Methods(2016-05) Pan, Erte; Han, Zhu; Ogmen, Haluk; Prasad, Saurabh; Pan, Miao; Li, Husheng; Qian, LijunWith the tremendous development of the modern complex networks such as smart grid and wireless communication domains, the data analysis tasks are significantly involved. In smart grid systems, there are emerging concerns on recognizing energy users' behavior patterns so that the energy trading companies are able to provide customized services. To understand users' usage patterns, efficient grouping methods are required such as clustering or nonparametric Bayesian models in machine learning field. In wireless communication field, a heat topic of locating personal devices and trajectory analysis is drawing more and more attention with the development of advanced personal devices such as the smart phones. Given this background, this dissertation provides a theoretical research in smart grid systems and wireless communications networks with emphases on probabilistic clustering analysis, pricing scheme design, sublinear sampling, tensor voting theory and trajectory pattern recognition. The main contributions of this dissertation include: a comprehensive overview of basic concepts, models and state-of-the-art techniques used in smart grid and trajectory analysis is provided; a novel distance measurement for clustering analysis is proposed from the probabilistic point of view. Moreover, the stopping rules and clustering quality problems have been investigated with proposed novel metrics; the pricing schemes design has been formulated into an optimization problem. The novel sublinear sampling algorithm has been developed to address the computation efficiency in the context of big data; the tensor voting theory has been introduced to the trajectory inference problem and is implemented in the sparse sense to facilitate the computation. The fractal analysis has been employed as a novel method to extract trajectory features for trajectory pattern recognition tasks.Item Tensor Voting Techniques and Applications in Mobile Trace Inference(IEEE Access, 12/24/2015) Pan, Erte; Pan, Miao; Han, ZhuInitially appearing as an abstract object frequently used in math and physics, tensors have been attracting increasing interest in a broad range of research fields, such as engineering and data science. However, a few studies have addressed their application in wireless scenarios. In this paper, we investigate the wide applications of tensor techniques with an emphasis on the tensor voting method, which serves as an artificial intelligence approach for automatic inference and perceptual grouping. To illustrate the efficiency of the tensor voting approach, we tackle the tracking problem of inferring human mobility traces, which can provide key location information of networking objects. The trace inferring problem is considered under the circumstance that the recorded location information exhibits missing data and noise. Based on the tensor voting theory, we propose a sparse tensor voting algorithm and an implementation scheme with computational efficiency. The model is constructed based on the geometric connections between the input signals and encodes the structure information in the tensor matrix. The missing location information and noise can be distinguished via tensor decomposition. Once the trace information has been completed, further analysis of the inferred trace can be performed based on feature extraction to differentiate different objects. Moreover, we propose several feature extraction methods to characterize the inferred trace, including the scale invariant feature obtained from the fractal analysis. The proposed methods for trace completion and pattern analysis are applied to real human mobility traces. The results show that our proposed approach effectively recovers human mobility trace from the incomplete and noisy data input, and discovers meaningful patterns of inferred traces from various objects.