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Complex systems are prevalent in many fields such as finance, security and industry. A fundamental problem in system management is to perform diagnosis in case of system failure such that the causal anomalies, i.e., root causes, can be identified for system debugging and repair. Recently, invariant network has proven a powerful tool in characterizing complex system behaviors. In an invariant network,...
Significant increases in computational resources have enabled the development of more complex and spatially better resolved weather and climate models. As a result the amount of output generated by data assimilation systems and by weather and climate simulations is rapidly increasing e.g. due to higher spatial resolution, more realisations and higher frequency data. However, while compute performance...
A ship maneuvering simulator is elïective training tool for navigation officers. An instructor of simulator training executes the evaluation based on the check list. The trainee is told the evaluation at the de-briefing, and obtains the chance to improve his maneuvering skill. On the other hand, some objective evaluation methods for the simulator training were proposed, and also a method for estimating...
The paper presents an effective identification method for DDoS attacks and flash crowd in the source-end network. As DDoS attack and flash crowd behavior dramatically increase the number of new (or forged) source IP addresses, the method firstly construct a time series by counting the number of new (or forged) IP addresses in the monitored local area network, and use VTP (variance-time plots) method...
Multiscale entropy (MSE), an estimate of the complexity of physiological signals has been used for assessing diabetes status. This method requires much computation effort. Our study aimed to examine the Poincaré plot, an easier method for computation to differentiate the diabetes status. We selected subjects and divided them into three groups including the non-diabetes (HbA1c ≤ 6.5%, n=22), diabetes...
Two standard assumptions of the classical blind source separation (BSS) theory are frequently violated by modern data sets. First, the majority of the existing methodology assumes vector-valued signals while data exhibiting a natural tensor structure is frequently observed. Second, many typical BSS applications exhibit serial dependence which is usually modeled using second order stationarity assumptions,...
One of the prospective signals to assist in the acquisition and examination of heart rate variability (HRV) is the photoplethysmographic (PPG) time series. Poincaré method has been applied to HRV analysis in a patient with diabetes and chronic renal failure (CRF). Our study develops the Multiscale Poincaré (MSP), an easier method for computation to assess diabetes. We selected subjects and divided...
This paper discusses a novel methodology for dynamic modeling of writing process. Sequent sub-documents of a given document are described through occurrences of the suitably selected N-grams. The Mean Dependence similarity measures the association between a present sub-document and numerous preceding ones and transforms a document into a time series, which is supposed to be weak stationary if the...
For a developing country such as India, to have the best usage of resources, public planning requires good forecasts of future trends. India's Index of Industrial Production (IIIP) is an index which conveys the status of production in the industrial sector of the economy. In this study, an artificial neural network (ANN) was applied to forecast IIIP. Accordingly, the inputs to the ANN consisted of...
An effective monitoring and analysis of ecosystems requires developing new tools and knowledge. In this paper, we propose an approach for detecting land-cover changes using satellite Image Time Series. This approach represents each image by spectral indices and then extracts local features of these representations. Next, a clustering technique (e.g., k-means) is applied to the extracted features,...
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...
The well-being of the environment is one of the major factors that contributes to sustainability. Sustainable human settlements require local governance to plan, implement, develop, and manage human settlements expansions. This is important as the number anthropogenic activities is directly correlated to the increase in human population within a geographical region. Regional mapping of land cover...
In this research, we proposed the Synchro Water Index (SWI) to detect widespread inundation extent in a transboundary river basin using the time-series Moderate Resolution Imaging Spectrometer (MODIS) data, a major contributor to progress in international flood monitoring. After removing clouds using the White-object Index (WoI), the multi-temporal processing coupled with in-situ water level data...
Unmanned aerial vehicle (UAV) images have great potential for agricultural researches because of their high spatial and temporal resolutions. However, most UAV researches in the agriculture field have adopted vegetation indices without second derivative parameters related with a growth model. In addition, visible band vegetation indices in UAV researches have not been explored in detail despite of...
Change detection (CD) between a pair of images is a popular problem in remote sensing. Despite a large amount of data is acquired every day by remote sensing satellites, standard CD methods usually consider only the two target images between which we desire to detect changes. The aim of this work is to present a novel framework in which the bi-temporal CD is redefined by evaluating the consistency...
The Detrending Moving Average (DMA) algorithm can be implemented to estimate the Shannon entropy of a long-range correlated sequence which will be shown to be of particular relevance for its significance in finance. The entropy is written as the sum of two terms corresponding respectively to power-law (ordered) and exponentially (disordered) distributed blocks (clusters). Interestingly, the behaviour...
We addressed two areas of concern regarding the analysis of a financial time series with a correlation structure, coarse graining (or renormalization) and the extraction of leading and lagging structures. We introduce the complex Hilbert principal component analysis to solve these two problems, and apply them to the time series of 33 Tokyo Stock Exchange industry indices and Tokyo Stock Price Index...
Parameters describing the cyclic patterns of vegetation phenology can be derived from satellite image time series. The length of the annual vegetation period is a useful summary parameter, important for analyzing broad-scale ecological differences of vegetation and seasonal gains in biomass. Anomalies in season length can be linked to principal seasonal drivers of vegetation growth, namely temperature,...
The aim of this study is to assess the potential of satellite image time series with high spatial and high temporal resolutions for the prediction of grasslands plant biodiversity. The grasslands are modeled at the object scale to be consistent with ecological measurements (one biodiversity index per grassland). A kernel regression is used to predict the biodiversity index of a grassland from its...
Brazilian official agricultural statistics time series data on agricultural production from 1980 until today and census data at municipality level can show trends and consequences of agricultural and sugarcane area expansion, as well as, pasture area and livestock production. Detailed analysis is needed, considering for example the geographical distribution, the types of systems involved (e.g. purely...
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