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Canonical correlation analysis(CCA) is a popular technique that works for finding the correlation between two sets of variables. However, CCA faces the problem of small sample size in dealing with high dimensional data. Several approaches have been proposed to overcome this issue, but the resulting transformation matrix fails to extract shared structures among data samples. In this paper, we propose...
In reservoir engineering, there is always a need to estimate crude oil Pressure, Volume and Temperature (PVT) properties for many critical calculations and decisions such as reserve estimate, material balance design and oil recovery strategy, among others. Empirical correlation are often used instead of costly laboratory experiments to estimate these properties. However, these correlations do not...
RNN Encoder-Decoder and attentional mechanism have lately been used to improve neural machine translation (NMT) on bilingual parallel corpus. In this paper, we propose tri-lingual NMT. Based on the Encoder-Decoder and attentional mechanism, we translate source language to target language, meanwhile translate another parallel source language to target language. We provides two approaches called splicing-model...
A novel multiscale phase congruency (MPC) based analysis method is proposed in this paper for edge saliency detection and non-salient region texture suppression. Several MPC maps are proposed to be merged. Gaussian function based center priors and threshold processing are applied for the final edge saliency map generation, which can effectively suppress the textures and the detailed edges of non-salient...
The random output of doubly fed induction generator is processed by the method of scene analysis, and on this basis, a mathematical model of reactive power optimization based on the minimum net loss is established. For reactive power optimization based on particle swarm optimization algorithm, the algorithm is prone to existing “premature convergence” problem, which leads to poor optimization results,...
In order to discretize the continuous-time neural dynamics or control system effectively and to achieve high computational accuracy in approximating the first-order derivative of a target function, a 1-node-ahead differentiation formula within Zhang finite difference (ZFD) framework labeled as 4-node g-square finite difference (4NgSFD) is presented in this paper. The presented formula, which is based...
In nondestructive testing of anchor bolt quality, it is important to identify the state of the anchor bolt accurately. In this paper, a combination method of spectral kurtosis and K-means clustering algorithm is proposed to identify different types of anchor models: maximum and nonzero minimum values are extracted as eigenvalues from spectral kurtosis distribution of anchor model signals which was...
Machine learning with concept drifting attracts a lot of attention in recent years. However, there are only a few works on concept drift learning with imbalanced data. The Learn++.NSE, the Learn++.NIE, and the Learn++.CDS from the Learn++ family are three state-of-the-art learning algorithms designed to deal with machine learning with concept drifting. In this work, we firstly give a brief introduction...
Energy disaggregation or NILM is the best solution to reduce our consumption of electricity. Many algorithms in machine learning are applied to this field. However, the classification results from those algorithms are not as well as expected. In this paper, we propose a new approach to construct a classifier for energy disaggregation with deep learning field. We apply Gated Recurrent Unit (GRU) based...
Recent work on neural network models shows success in dependency parsing. In this paper, we present a sequence learning dependency parsing (SLDP) model using long short-term memory for shift-reduce parser. A feed-forward neural network is used to build greedy model from rich local features. With the features extracted by the local model, we further train a long short-term memory (LSTM) model optimized...
Human diseases of different etiologies, for example, different acute inflammatory diseases, often share common underlying molecular mechanism. Investigating this commonality will help us not only better understand the diseases but also identify new molecular targets for therapy. In our study, we make use of genomic profile data together with clinical information and propose a statistical approach...
Construction Grammar (CxG) with strong explanatory power for language phenomena and language learning is still a stranger for most of natural language processing (NLP) tasks. The main reasons include challenges brought by the opening definition of construction, the lacking of a large scale construction knowledge base, the lacking of annotation tools and construction-annotated corpus which are big...
Three-way decisions based Bayesian network is an integrated model by combining three-way decisions theory and Bayesian network. Compared to classical two-way decisions based Bayesian network, the three-way decisions based Bayesian network could obtain a lower misclassification error. Another advantage of three-way decisions based model is that it can generate minimal decision cost. Based on this,...
Uncertainty and variability are the inherent factors in modern power systems with increasing number of intermittent power resources such as wind and solar integration. This paper presents a novel probabilistic approach to evaluate the power grid with Fault Ride-Through consideration of the wind generation. Low Voltage Ride-Through regulation applied in China grid is investigated in this paper. Critical...
Leprosy, also known as Hansen's disease, is a debilitating and chronic bacterial infection. As per World Health Organization's report, there were 189,000 chronic cases of Leprosy in 2012 with 230,000 new diagnoses. Although curable at later stages, an early diagnosis prevents nerve involvement and the disabilities it incurs. The authors henceforth propose a Convolutional Neural Network based architecture...
This paper presents a novel modular soft robot, which can finish moving forwards. This intelligent system mainly consists of four deformable rubber cells, two friction feet, the solenoid valves, an air pump and the control systems. The size of each spherical cell and the shape of the soft robot can be changed, according to the appropriate inflating and deflating orders of the spherical cells. With...
In this paper, we consider decision systems which consist of preference ordered conditional attributes and symbolic decision attributes. Thus, dominance relations and equivalence relations can be respectively defined on the conditional attribute set and decision attribute set. Based on this combined dominance-equivalence relations, a fast positive-region reduction (FPR) algorithm is developed by introducing...
Opinion or sentiment analysis has risen to extract useful information from a lot of unstructured text data, in the form of customer reviews on different products and their features or online SNS data respectively. Customer reviews are not only helpful for potential customers, but also are helpful for the manufacturers of the products to raise their products and services. The reviews conciseness takes...
Influence diagram is a relatively straightforward and easy method to make risk analysis and evaluation. To a certain extent, fuzzy mathematics method is introduced into parametric modeling of risk influence diagram to solve difficult problems that the probabilities of basic events are not easy to get. The work describes the relationship of different influence factors in the risk management process...
To preserve privacy has been a difficult problem in designing secure cloud storage system. In existing cloud storage systems, the privacy protection of user file directories is ignored while the privacy protection of files is focused on. A privacy-preserving method of creation file directory in cloud storage systems is proposed in this paper. This method makes cloud service providers can't identify...
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