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A large number of aviation equipment maintenance data exhibit seasonal behavior, such as aircraft failure rate. Consequently, seasonal forecasting problems are of considerable importance in aviation maintenance support. Aircraft failure rate is an important parameter of aviation equipment RMS (Reliability-Maintainability-Supportability). It is indispensable to scientifically predict the aircraft failure...
Nowadays, in order to observe and control data centers in an optimized way, people collect a variety of monitoring data continuously. Along with the rapid growth of data centers, the increasing size of monitoring data will become an inevitable problem in the future. This paper proposes a correlation-based reduction method for streaming data that derives quantitative formulas between correlated indicators,...
Prediction of the behavior of the grouped users in the future is a meaningful research question. In this paper, we take the transit users as an example to introduce the phase space reconstruction method and use the massive sequence data to model the large-scale system with a dynamic evolution model. At the same time, considering the shortcomings of the general prediction method in large data set,...
The present paper addresses the problem of the estimation of the day-ahead generated power (GP) of a photovoltaic plant using predicted regional solar radiation (SR). The contribution is twofold. First, it investigates the problems related to the design of day-ahead SR predictors and then studies their combination with the prediction data obtained from a meteorological service. Different setups of...
Variations in sensor data collected from equipment have been widely analyzed by using anomaly detection methods for predictive maintenance. Our experience shows that correlations between sensors effectively predict failures because the correlations usually reflect the status of equipment with higher sensitivity. In this paper, we present a method that exploits correlations between sensors for pre-processing...
This paper proposes an efficient decoupling model for information producer (IPD) (i.e., physical sensor) and information provider (IPV) toward a semantic sensor-cloud integration to improve Wireless Sensor Networks' (WSN) lifetime. In particular, while IPDs produce sensing information, their IPVs, which are designed as virtual sensors on sensor-cloud based on network function virtualization, are responsible...
The recent proliferation of mobile devices embedded with capable sensors, provides an opportunity to the popular concept of mobile crowdsensing. By studying the correlation of crowd-sensed data in both spatial and temporal dimensions, we can get a clear understanding of the intrinsic pattern of data in mobile crowdsensing, which is the basic for further data analysis, such as data filtering, smoothing...
The choice of the order of Fuzzy Time Series model has, to some extent, an influence on the accuracy of the forecasted result. This paper establishes relationship between Autoregressive Model and Fuzzy Time series model so as to apply autocorrelation theory to the selection of the order of Fuzzy Time Series Model (FTSM). Therefore, instead of listing the forecasted results generated by Nth order model...
Granger causality based approaches are popular in unveiling directed interactions among brain regions. The present work advocates a multi-kernel based nonlinear model for obtaining the effective connectivity between brain regions, by wedding the merits of partial correlation in undirected topology identification with the ability of partial Granger causality (PGC) to estimate edge directionality. The...
In this paper we discuss a class of models for time series of low count data based on the Generalized Linear Model (GLM) approach. Unlike the traditional Auto-Regressive Moving-Average (ARMA) models for continuous Gaussian data, these models capture both the temporal correlation structure and the discrete marginal distribution of count data. We focus on the properties, parameter estimation, and model...
Scale-free dynamics commonly appear in individual components of multivariate data. Yet, while the behavior of cross-components is crucial in modeling real-world multivariate data, their examination often suggests departures from exact multivariate self-similarity (also termed fractal connectivity). The present paper introduces a multivariate Gaussian stochastic process with Hadamard (i.e., entry-wise)...
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,...
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,...
Today's dynamic computing deployment for commercial and scientific applications is propelling us to an era where minor inefficiencies can snowball into significant performance and operational bottlenecks. Data center operations is increasingly relying on sensors based control systems for key decision insights. The increased sampling frequencies, cheaper storage costs and prolific deployment of sensors...
We examine the use of the Fourier transform to discriminate dynamic behavior differences between congested and uncongested systems. Simulation continuous time statistic ‘trajectories’ are converted to time series for Fourier analysis. The pattern of Fourier component magnitudes across frequencies differs for congested versus uncongested systems. We use this knowledge to explore statistical process...
The tutorial will be used to introduce some basic techniques for analysing the output of stochastic simulation models. Using examples, we will describe methods for determining the optimal warm-up length and number of replications as well as introducing ways of using simulation to compare different systems.
In this paper we present our submission to the AAIA'16 Data Mining Challenge, where the objective was to predict dangerous seismic events based on hourly aggregated readings from different sensor and recent mining expert assessment of the conditions in the mine. During the course of the competition we have exploited a framework for automatic feature extraction from time series data that did not require...
The analysis of the wind speed is of great significance to the wind power system's stable operation. There are many methods of analysis at present, they describe the time series in the frequency domain only. But it is not enough. It is necessary to make a comprehensive description not only from the frequency domain but also the time domain. In this paper, we study the independence of time series in...
In this paper, the multidimensional output Gaussian process (GP) is applied to model urban environmental data collected by sensor networks. Measurements from sensors at different locations are correlated. Moreover, we observe that the pollution level in urban area is highly coupled with human activities and shows periodic patterns accordingly. Based on these observations, we discuss the design of...
In the design flow of multi-processing system-on-chips (MPSoCs), the evaluation of communications structures, particularly, networks on chip (NoCs), plays a very important role, since it may show relevant characteristics on performance, energy consumption or cost. Simulation under a number of stimulus given by a traffic generator is a relevant solution for MPSoCs performance analysis. Traditional...
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