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We present a scheme for determining the number of signals common to or correlated across multiple data sets. Handling multiple data sets is challenging due to the different possible correlation structures. For two data sets, the signals are either correlated or uncorrelated between the data sets. For multiple data sets, however, there are numerous combinations how the signals can be correlated. Prior...
With climate change, there is a higher possibility of drought. Drought is a critical threat to energy generation, specifically to the typical reusable energy source; hydropower in the Northeastern region in the USA, a region that relies heavily on hydropower. In order to draft mitigation strategies for the drought problem in the regions, we present 75% and more of monthly net electricity generation...
As for nonstationary signal, such as subpixel peak detection,we could be difficult to suppress the noise of super- Gaussian and sub-Gaussian in the mixed signal with the traditional low order filter. The gradient search method is generally adopt in the filter algorithm based on higher order statistics, but it is difficult to avoid local convergence and large complexity in the gradient search process...
Recursive Principal Components Analysis is explored as a method to identify and classify fault sources in a 12MW steam dual fuel power plant. The algorithm assessment is performed off-line by using data of relevant plant wide-information. A simple contributions matrix based in normalized data is proposed to diagnose plant faults. Results indicate it is possible to detect, classify and possibly even...
Gene selection is an important step in analysis of gene data sets in which the number of genes exceeds greatly the number of samples. In this paper, we propose a new method that uses a random forest model to select genes from high dimensional gene data sets. In this method, Breiman's random forest algorithm is first used to generate a random forest model from a high dimensional data set. Then, features...
Atomic spin gyroscope is a new kind of gyro based on quantum mechanics, with ultra-high precision, simple structure, small size, etc. Therefore, to study the characteristics of random drift for improving the accuracy of atomic spin gyro is significant. Firstly, through the analysis of the gyro static output data and preprocessing, the stationary time series gyro random error has obtained. Then established...
Trying to PM2.s as the independent variable to establish the “beam-diffuse radiation separated” model, factor analysis showed that, PM2.5 and diffuse ratio have positive correlation. Based on the clearness index Kt and sunshine hours n/N as variables, the polynomial model and the BP neural network algorithm model containing PM2.5 as the independent variable is proposed, the polynomial model is fitted...
The shift from centralised large production to distributed energy production has several consequences for current power system operation. The replacement of large power plants by growing numbers of distributed energy resources (DERs) increases the dependency of the power system on small scale, distributed production. Many of these DERs can be accessed and controlled remotely, posing a cybersecurity...
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...
Currently, the requirements of service quality in the electric power data network are getting higher and higher, and traffic prediction is an important premise to promote service quality. In order to accurately predict the total traffic of communication channels, a Multi-Applications Comprehensive Traffic Prediction (MACTP) model is proposed in this paper. Differing from F-ARIMA and S-ARIMA models...
Today, the use of learning analytics is becoming more crucial in the learning environment for the purpose of understanding and optimizing students' learning situations. The purpose of this paper is to examine the impacts of Teacher Interventions (TIs) on students' attitudes and achievements involved with the lesson by analyzing their freestyle comment data after every lesson. The current study proposes...
In Vietnam, environmental data collected from ground-based stations may contain abnormal or missing values due to several problems during operation, i.e. sensor's problems. This paper proposes a standardization procedure which try to detect unusual values and fill in missing data. Experiments were conducted for PM10 data. Two datasets measured in 01/2011 and 01/2012 at Nguyen Van Cu station in Hanoi,...
The users wish to search for fewer data sources and retrieve better quality results, so the data source selection becomes the core technology in the deep web data integration. In the data source selection, it normally considers both the data source correlation to the user's query and the document content duplication. We propose a new two step data source selection strategy by first ranking on data...
The concept of pairing confidential-relevant variables (connected variables) using ridge regression and bootstrap sampling has recently been proposed for developing perturbation models to data privacy in cyber-physical systems. In this approach, a single set of perturbation parameters for all the pairs of connected variables has been used to achieve trade-off between data confidentiality and classification...
An important goal of the renewable energy law is to promote new energy enterprises to better take the social responsibility, so, the research on the impacts of the law to the correlation between CSR and Financial Performance is meaningful. Using the panel data regression model, we make a comparison about the relevance of renewable energy corporate social responsibility and financial performance before...
Correctly predicting the passenger flow of an air route is crucial for the airline company to make sales policy. Because of the uncertainties and data inadequacy in the passenger flow prediction of the civil aviation, regression analysis and a grey prediction method are used for predicting and analyzing the passenger flow of the air route in 2016 based on the data of to-and-fro air route of an airline...
It is certain that the individual learners should be different from each other in order for a committee machine to reach the better performance. However, differences alone among the individual learners are not enough for the committee machine to predict well on the unknown data. It would be essential for each individual learner to be able to decide whether to learn to be different or not to the other...
Recently, there are many big data analytic models which have been developed. In this paper, we consider the spatial panel model and extend the STIRPAT model. These two models are integrated for us to investigate the effects of the urbanization on energy consumption in China. Our study is based on the panel data of 29 provinces in China from 2002 to 2013. Several conclusions are made from our investigation...
The accurate price forecasting of electricity market is crucial for profit maximizing producers and consumers in liberalized power markets. In all market places (day-ahead, intra-day and real-time) accurate price prediction is needed to generate optimal bids and maximize the profit. This paper first presents three methods for forecasting day-ahead market prices, namely Generalized Autoregressive Conditional...
This study has a purpose to investigate the adoption of online games technologies among adolescents and their behavior in playing online games. The findings showed that half of them had experience ten months or less in playing online games with ten hours or less for each time playing per week. Nearly fifty-four percent played up to five times each week where sixty-six percent played two hours or less...
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