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This extended summary introduces our recent work on phasor measurement unit (PMU) data analysis by exploiting the low-dimensional structures in spatial-temporal blocks of PMU data. An efficient online missing data recovery algorithm is proposed to leverage the low-rank property of the Hankel PMU data matrix to recover data losses even when all the measurements are lost simultaneously. A unified approach...
Emergence of big data is directly proportional to the data shared in social media. Audio, video, text or the combination of all the above are the data shared in social media. Social networking is achieved by Social Networking Sites (SNS). In real world business, analysts use software tools to analyze product sales, promotion of brand and also tend to identify influential factors that impact their...
Non-technical losses (NTL) in electricity distribution are caused by different reasons, such as poor equipment maintenance, broken meters or electricity theft. NTL occurs especially but not exclusively in emerging countries. Developed countries, even though usually in smaller amounts, have to deal with NTL issues as well. In these countries the estimated annual losses are up to six billion USD. These...
Vehicle safety airbags can protect occupants from vehicle collisions, but there are still some shortcomings, thus need to be constantly improved. In occupant restraint system simulation, the establishment of a reasonable airbag model determines whether the simulation results are consistent with the actual test results. The aim of this study is to establish a precise airbag model for vehicle occupant...
The random forest algorithm is a new classification and prediction model algorithm. So far, there is not much research on the problem of unbalanced data for random forest classification, ditto, no direct and effective method. On the basis of feature selection algorithm based on correlation measure, the integration feature selection method was helpful to increase the selection probability of classification...
This paper proposes a state space model to describe multivariate autocorrelated zero-inflated count series. The model extends the classical zero-inflated Poisson distribution into multivariate cases but is able to impose different zero inflations on different dimensions. Combing the zero inflation with the log-normal mixture of independent Poisson distribution, this model allows for flexible cross-correlations...
In developing countries, the issue of road accidents are a major concern. Increasing road traffic/vehicle occupancy could be the reason behind this. There is an increase in accidents over the years. It is very important to regulate traffic on roads to reduce accidents in accident prone zones. To reduce accidents, it is very important to analyze and identify such road accident prone features. Based...
The result of Chinese housing market continues to prosper or not is related to the development of China, and further it also has an impact on the world finance. Thus forecasting the house price index is very important and challenging. In this paper we propose an unsupervised learnable neuron model (DNM) by including the nonlinear interactions between excitation and inhibition on dendrites. We use...
Because of the volatility of memory, nodes in in-memory storage system crashing down would lead to data lost. One solution to this problem is backing data up. However, if we backup data to a node which is about to fail down, the data should be recopied again. That would lead to a large amount of backup data, and in turn reduce the system reliability. We first establish a correlated failure model with...
The statistical properties of traffic in Internet access networks have long been of interest to networking researchers and practitioners. In this paper, we analyse network traffic originating and terminating from various types of Internet access networks (Ethernet, Digital Subscriber Line, Wireless hotspot and their next tier Internet Service Provider's core network) and show that renewal processes...
Stereotactic radiotherapy such as Cyberknife is one of the main methods of treatment for lung cancer, but tumor location change caused by human respiration has brought great difficulties to accurate radiation therapy. The main method to reduce the effect of respiratory motion in the process of radiotherapy is respiratory motion real-time tracking technology. The basis of real-time tracking is establishing...
The clustering algorithm by fast search and find of density peaks is shown to be a promising clustering approach. However, this algorithm involves manual selection of cluster centers, which is not convenient in practical applications. In this paper we discuss the correlation between density peaks and cluster centers. As a result, we present a new local density estimation method to highlight the uniqueness...
Prices of derivative contracts, such as options, traded in the financial markets are expected to have complex relationships to fluctuations in the values of the underlying assets, the time to maturity and type of exercise of the contracts as well as other macroeconomic variables. Hutchinson, Lo and Poggio showed in 1994 that a non-parametric artificial neural network may be trained to approximate...
Reliable information processing is an indispensable task in Smart City environments. Heterogeneous sensor infrastructures of individual information providers and data portal vendors tend to offer a hardly revisable information quality. This paper proposes a correlation model-based monitoring approach to evaluate the plausibility of smart city data sources. The model is based on spatial, temporal,...
With no limit on time and location [1], the number of users attracted by massive open online course (MOOC) has increased rapidly, and many platforms have been built to provide a variety of courses. All of these trigger an explosive growth in data volume. As we known, people have met big data in many areas and proposed many techniques and methods to deal with them. However, people still have no sense...
Motivated by real applications, heterogeneous learning has emerged as an important research area, which aims to model the co-existence of multiple types of heterogeneity. In this paper, we propose a HEterogeneous REpresentation learning model with structured Sparsity regularization (HERES) to learn from multiple types of heterogeneity. HERES aims to leverage two kinds of information to build a robust...
Deep convolutional networks have achieved successful performance in data mining field. However, training large networks still remains a challenge, as the training data may be insufficient and the model can easily get overfitted. Hence the training process is usually combined with a model regularization. Typical regularizers include weight decay, Dropout, etc. In this paper, we propose a novel regularizer,...
Multi-label learning is widely applied in many tasks, where an object possesses multiple concepts with each represented by a class label. Previous studies on multi-label learning have focused on a fixed set of class labels, i.e., the class label set of test data is the same as that in the training set. In many applications, however, the environment is open and new concepts may emerge with previously...
We introduce a novel dynamic model for discrete time-series data, in which the temporal sampling may be nonuniform. The model is specified by constructing a hierarchy of Poisson factor analysis blocks, one for the transitions between latent states and the other for the emissions between latent states and observations. Latent variables are binary and linked to Poisson factor analysis via Bernoulli-Poisson...
Discovering and modeling lead-lag relations is a critical task in a variety of domains, including energy management, financial markets and environment monitoring. This task becomes more challenging when processing massive and highly dynamic data sources, often produced by sensors and live feeds that collect data about evolving entities in the real world. To cope with this data volume and velocity,...
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