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Discovering and analyzing networks from non-network data is a task with applications in fields as diverse as neuroscience, genomics, energy, economics, and more. In these domains, networks are often constructed out of multiple time series by computing measures of association or similarity between pairs of series. The nodes in a discovered graph correspond to time series, which are linked via edges...
In this article, we propose a data analytics development to detect unusual patterns of flights from a vast amounts of FDR (flight data recorder) data for supporting airline maintenance operations. A fundamental rationale behind this development is that if there are potential issues on mechanical parts of an aircraft during a flight, evidences for these issues are most likely included in the FDR data...
The temperature and dew point records of seven weather stations, located in India have been scrutinized under the self-organized criticality regime. The data models an almost ideal scaling behaviour as of the type of t/fα noise; with α≈1. This scaling behaviour strongly suggests the presence of self-organized criticality (SOC) behind both the signals. To draw more insight into the detailed dynamics...
The objective of this work is to detect delay times between two ultrasonic signals, one of them considered as a reference and the other signal as the signal to be evaluated, using the wavelet transform, to indirectly estimate temperature changes. In this work the evaluation of 3 types of wavelets (Morlet, Mexican Hat and Daubechies 5) was made to detect the delay time in echo ultrasonic signals which...
The scale of the modern city has been expanding, which leads to a lot of serious environmental pollution problems. Among them, the air pollution problem is the most prominent. In order to control the air pollution in urban cities, the government has deployed a lot of air pollutant monitoring equipment which produce massive multi-dimensional time series data. Through the motif discovery and analysis...
The JOCOR algorithm, which was first introduced by Mueen et at., is the currently exact method for joining two time series to find the most correlated subsequence. Despite the fact that time complexity for JOCOR belongs to O(n2lgn), where n is the length of the time series, it becomes inefficieit and unfeasible even for medium-size time series. This is because the JOCOR method is based on a brute-force...
PPP (Precise Point Positioning) is a relatively new GNSS (Global Navigation Satellite System) method that enables position determinations using a single receiver. Being able to survey with a single receiver makes this approach more attractive and affordable than other GNSS positioning techniques. Since a single receiver is used, a drawback of this technique is propagation of errors such as orbit and...
This paper discusses the motivation and implementation for Cray's Project Caribou. Project Caribou enables users to correlate HPC job performance with Lustre file systems through collected metrics and events. We will discuss use cases, the sources of metrics that are collected, correlation, and how the data is visualized. Additional topics to include events and alerts that are available, as well as...
In this paper, we propose a novel technique for the estimation and correction of breathing artifacts in thermographic images. Our proposed technique combines a phase correlation method (PCM) to find large displacements and an optical flow method (OFM) to determine small displacements (sub-pixel accuracy). In the first step, each image is divided into a sequence 10 × 10 pixel sub-images and then the...
Advanced spatio-temporal electric load modeling and accurate spatio-temporal load forecast are essential to both short-term operation and long-term planning of power systems. This paper explores the spatio-temporal dependencies of electric load time series. The Southern California feeder load data show that feeders which are spatially close to each other share a more similar load pattern than those...
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...
Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper, we propose an approach based on the shape-behaviour relation...
While adding new capabilities, the distributed energy resource proliferation raises great concern about challenges such as dynamic fluctuations of voltages. For example, in a volatile setting with highly uncertain renewable generation and customer consumption, it is challenging to provide reliable power and voltage prediction for operational planning purposes to mitigate risks, e.g., over-voltages...
Radio links operating at frequencies above 10 and 20 GHz suffer signal degradation and losses due to hydrometeor phenomenon such as rain. This process is called rain fade. The fade due to rain usually leads to outage or complete loss of signal. Rain fade can be simulated using rain rate and it can be obtained from existing meteorological datasets such as rain gauges and rain radars. To properly simulate...
There are some uncertainties associated with the influencing factors in the stock price forecasting model. Main influencing factors of stock price are selected by gray relational analysis, and the main influencing factor was used as an exogenous variable to establish the ARMAX model to forecast the stock price. Taking PetroChina as an example to carry out case analysis, the result shows that the fitting...
Disturbances originating in one control loop of a large industrial plant can propagate far from the source, giving rise to plant-wide oscillations. The underlying interactions among the different control loops make it hard to identify the origin of such large scale disturbances. This paper studies the application of the convergent cross mapping (CCM) based technique to isolate the source of a plant-wide...
Anomaly detection based on telemetry data can improve the operating safety for spacecrafts. Most of the anomaly detection methods in this domain are based on Euclidean distance for similarity measure of monitoring parameters. However, the Euclidean distance has many limitations on telemetry data similarity measure and may affect the detecting performance. Therefore, improved distance measures and...
Spacecraft state prediction is an important method to solve the related problems of spacecraft health management, fault diagnosis and so on. Chaos theory is widely used in electric power and machinery industries. It can describe the whole system with some parameters. It is proved that spacecraft telemetry parameters are chaotic by calculating the chaotic characteristics. Aiming at the problems of...
Previous studies have shown that changes in human emotions or public opinions can have an impact on volatility of stock market. In this paper, we make use of the unstructured comments data from the stock forum on the Shanghai Composite Index to generate the structural emotion time series of the stock market based on a series of methods including word segmentation, feature extraction, machine learning...
In our analysis, we consider daily rainfall records y(n) from 3825 areas covering entire China. The daily rainfall records is 50 years long from 1961 to 2011. We study the statistical properties of the daily rainfall data and return intervals Tq between two consecutive rainfall records above some threshold q. Using the detrended fluctuation analysis (DFA) method to analyze the long-term correlation...
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