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This paper presents an optimization algorithm used for energy controllers. Residential users can define the beginning and terminating times of elastic electricity use. Real-time pricing information is provided through smart meters by a utility company. Users can make informed decisions regarding energy scheduling to minimize total electricity tariff. In the proposed method, the elastic electricity...
In this paper, we propose a muscle gesture-computer interface (MGCI) system for a five-fingered robotic hand control employing a commercial wearable MYO gesture armband. Eight channels of surface EMG (sEMG) signals were acquired and segmented. Then four levels of Daubechies 5 Wavelet family were performed to analyze the EMG signal. Totally 72 features were extracted from the EMG raw data for 16 hand...
Publishing individual-related data for big data analysis such as scientific research and merchant analysis has become frequent in this decade. Most of these data can be represented as graphs, with real world entities as graph nodes and interrelationships among entities as graph edges. Mining these released data, or corresponding graphs, may facilitate the forming of judicious strategies for marketing...
An epileptic seizure is a brief episode of symptoms due to abnormal excessive or synchronous neuronal activity in the brain. Epilepsy is a chronic disorder that affects people of all ages, and one of the most common neurological diseases globally. Epilepsy can have both genetic and acquired causes, nevertheless, the exact mechanism of epilepsy is still unknown. It merits further investigation under...
Coronary artery diseases are the most common type of heart disease. Early detection and quantification of coronary plaques is therefore of high interest. CTA has rapidly emerged, and is nowadays widely used in clinical practice. A calcification detection and quantification method is proposed, which can detect the calcium plaque and quantify the stenosis of coronary artery in CT images. Firstly, the...
Extreme Learning Machine (ELM) is an algorithm for training single hidden layer feed-forward neural networks (SLFNs). Because ELM does not need the process of iterative learning, it is extremely faster than traditional learning algorithms such as back propagation algorithm and support vector machine. In ELM, the optimal solution with least squares norm is found by calculating the generalized inverse...
The Bedrosian identity has considerable importance for the nonlinear and non-stationary signal processing. In this paper we develop a unified mathematical treatment for Bedrosian identity. In order to study the Bedrosian identity of real-valued functions, we turn to study the equation F(z)G(z) = H(z), where G(z) and H(z) are both analytic functions belonging to Hardy spaces. Either the function F(z)...
In this paper, we present a new definition and generation of discrete 3D lines using stacked 2D matrixes. The new definition of 3D lines based on vertical projection and polar coordinate representations of two 2D lines will be deduced in theory. Assuming the Cartesian coordinates of a space point on the 3D line are known, the new representation of a 3D line is simplified to two angles: azimuth ϕ and...
In this paper, singular value decomposition is applied to detect the co-clusters in mutant-atom matrix data of solid angle. A set of significant patterns are found in the given matrix data with coherent solid angle values on both mutants and atoms. These patterns could provide clues about the main mechanism associated with drug resistance. In one pattern, all the mutants have the same response level...
According to this paper, the agricultural circular economy evaluation system is constructed by four aspects, which contains reducing resources input, environmental security and quality, economic and social development standard and recycle of resource. There are fourteen indicators, which is based on the agricultural circular economy concept and the basic principles. Meanwhile, this paper's target...
The increasing number of skyscrapers along with the large number of tall bridges throughout the world also increases the demand of a robust, automated and remotely controlled health monitoring system for civil architectures. It is very difficult and sometimes not feasible to inspect the structures whose heights are beyond the limit of an average traditional structure of the same type. Therefore, in...
Recently, the deep learning based methods, especially the ones based on convolutional neural network (CNN), achieved remarkable progresses in sentiment analysis. However, the CNN based methods do not take the latent topic in text into consideration. In this paper, we propose a CNN based Diversified Restrict Boltzmann Machine (RBM) method to model the sequence level latent topics in the sentences for...
Self-organizing map (SOM) is regarded as a type of feedfoward neural network. It has been successfully used for unsupervised learning. However, the objective function of the traditional SOM relies on the mean squared error (MSE) criterion, which makes the performance of SOM become poor in the presence of noise. In the paper, correntropy based measure is proposed to substitute MSE to enhance the anti-noise...
Most of existing hashing methods for image retrieval problems assume all images are given at the beginning. However, in some image retrieval problems, images may arrive or be labeled in an online or streaming manner. Current online hashing methods are fully supervised which assume all images come with labels. However, in real world big data environments, it is infeasible to have a fully labeled image...
Concept Factorization (CF) is a modified version of Nonnegative Matrix Factorization (NMF) and both of them have been proved to be effective matrix factorization methods for dimensionality reduction and data clustering. However, CF is essentially an unsupervised method which cannot utilize any prior knowledge of data. In this paper, we propose a new semi-supervised concept factorization method, called...
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...
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