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The objective of this paper is to address a new method based on trend extraction for isolating faults in the nonstationary and nonlinear processes. Firstly, a concise review of the traditional methods for fault isolation based on Hotelling statistic are introduced, a rigorous analysis of their weaknesses, especially the smearing (coupling) phenomena, is provided, and the possible handling strategies...
Since RDF triples are modeled as graphs, we cannot directly adopt existing solutions from relational databases and XML technologies. Thus, there are still a number of open problems in the area of Linked Data. We present a hybrid method between centralized and distributed approaches. By using auxiliary indexes based on the MBB approximation, our approach can retrieve distributed Linked Data efficiently...
In the emerging field of big data, a large volume of data has to be managed, operating on data of huge volume becomes easier when it's sorted and structured. The data can be structured using a simple algorithm i.e. index algorithm which stores and categories data on basis of their application. This in turn will be very beneficial on business level as well as on software level.
To mitigate the uncertainty of variable renewable resources, two off-the-shelf machine learning tools are deployed to forecast the solar power output of a solar photovoltaic system. The support vector machines generate the forecasts and the random forest acts as an ensemble learning method to combine the forecasts. The common ensemble technique in wind and solar power forecasting is the blending of...
This paper proposes an improved cloud-element model for information security risk assessment. Combined with the characteristics of dynamic balance of information security risk, we propose the control means and uncontrollable factors as information security risk evaluation index. The method of constructing the sample cloud has been expatiated, and the correlation between the cloud droplet and the sample...
This paper concerns the fundamental problem of processing conjunctive queries that contain both keyword conditions and range conditions on public clouds in a privacy preserving manner. No prior Searchable Symmetric Encryption (SSE) based privacy-preserving conjunctive query processing scheme satisfies the three requirements of adaptive security, efficient query processing, and scalable index size...
Water quality becomes one of the important quality factors for the quality life in smart cities. Recently, water quality has been degraded due to diverse forms of pollution caused by disposal of human wastes, industrial wastes, automobile wastes. The increasing pollution affects water quality and the quality of people's life. Hence, water quality evaluation, monitoring, and prediction become an important...
Water scarcity is one of the serious problems that California is facing today. Water scarcity leads to doughtiness when not properly addressed. In the recent years, California faces a serious drought problem. This provides a strong demand in building a real-time system to support water resources analysis, drought modeling and prediction. Existing models and approaches lack of desirable accuracy in...
This study explores the application of artificial intelligence on the causal relationship between mining production index and electricity load. The data used is the total mining production index and total electricity consumption in the mining sector sampled on a monthly basis from January 1985 to December 2011 in South Africa. Optimally-pruned and basic extreme learning machines were used to develop...
There is an increasing demand to explore similar entities in big graphs. For example, in domains like biomedical science, identifying similar entities may contribute to developing new drugs or discovering new diseases. In this paper, we demonstrate a graph exploration system, called GQFast, which provides a graphical interface to help users efficiently explore similar entities. Methodologically, GQFast...
We present two compact representations of rasters, which are used in GIS to represent temperatures, elevations, and other spatial attributes, that support queries on the positions and/or the values stored. These representations are based on space-filling curves and recent advances on compact data structures. They are practical, competitive with recent works on the problem, and present some improved...
The curved debris separated from rockets, the most objects frequently appeared in the threat complex, make the classification of ballistic targets very hard. However, rocket debris has not drawn enough research attention and current data generative model of convex objects is not applied to non-closed curved debris. This paper explores the method of modeling the infrared (IR) irradiance signatures...
The popularity of intelligent big data era in the community, making online shopping behavior play a more and more important role in the e-commerce model. This paper evaluated the operation efficiency of the electric business group buy site scientifically by puts forward the improved rough set method and fuzzy comprehensive evaluation method. First, on the basis of the analysis of the influence factors...
In view of the high-dimensional data stream characteristics of Omni-Channel consumer behavior, the synopsis generation algorithm of the Omni-Channel consumer segmentation was created based on the Omni-Channel customer value. The evolutionary data stream clustering algorithm of Omni-Channel consumer segmentation was proposed based on time attenuation model and sliding window model, which deal with...
The development of logistics and regional economy is complementary to each other, and the study of logistics efficiency is an important foundation to promote regional economic development. Based on the DEA model, this paper constructs the corresponding input-output index, and uses spss software to analyze the reasonableness of the selected indicators. It takes eight cities in the Yangtze Delta of...
This paper considers the problem of estimating an unknown high dimensional signal from (typically low-dimensional) noisy linear measurements, where the desired unknown signal is assumed to possess a group-sparse structure, i.e. given a (pre-defined) partition of its entries into groups, only a small number of such groups are non-zero. Assuming the unknown group-sparse signal is generated according...
Functional magnetic resonance imaging (fMRI) has provided a window into the brain with wide adoption in research and even clinical settings. Data-driven methods such as those based on latent variable models and matrix/tensor factorizations are being increasingly used for fMRI data analysis. There is increasing availability of large-scale multi-subject repositories involving 1,000+ individuals. Studies...
Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise...
Due to the highly predictable daily movements of citizens in urban areas, mobile traffic shows repetitive patterns with spatio-temporal variations. This phenomenon is known as Tidal Effect analogy to the rise and fall of the sea levels. Recognizing and defining traffic load patterns at the base station thus plays a vital role in traffic engineering, network design and load balancing since it represents...
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