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Short-term forecasting tool for wind power generation (WPG) can effectively enhance the scheduling of dispatchable generation and avoid wind power (WP) curtailment. This paper presents a Group Method of Data Handling (GMDH)-Neural Network (NN) approach for forecasting wind speed (Ws) and wind power output (WPO) in the short-term. Wavelet denoising is used to filter the high frequency outliers in the...
Recently, automatic modulation classification techniques using convolutional neural networks on raw IQ samples have been investigated and show promise when compared to more traditional likelihood-based or feature-based techniques. While likelihood-based and feature-based techniques are effective, making classification decisions directly on the raw IQ samples allows for reduced system complexity and...
SVM (Support Vector Machine), a state of the art classifier model is implemented on a computational mobile platform and its performances are evaluated against a low complexity classifier such as SFSVC (Super Fast Vector Support Classifier) on the same platform. For a better comparison, similar implementation for the two architectures are considered, such as using the same basic linear algebra library...
Hardware Trojans (HTs) have been generally inserted at the lower levels of the digital system design and fabrication process, where, due to the high complexity of the hardware model, their detection is more difficult. However, RTL models are becoming more and more complex, making difficult the identification of malicious behaviours also at this level. Unfortunately, only a few verification techniques...
In this paper, several ensemble cancer survivability predictive models are presented and tested based on three variants of AdaBoost algorithm. In the models we used Random Forest, Radial Basis Function Network and Neural Network algorithms as base learners while AdaBoostM1, Real AdaBoost and MultiBoostAB were used as ensemble techniques and ten other classifiers as standalone models. There has been...
Predicting zeroes precisely and rapidly after a fault initiation is the basis of controlled fault interruption. However, none available algorithms could predict current zeroes within several milliseconds. The objective of this paper is to propose a fast estimation algorithm that can predict current zeros within 3ms after fault initiation. An algorithm is proposed based on an improved BP network. In...
This paper proposes a neural network (NN) approach for demodulating output signals of a nonlinear channel with memory. The feed-forward neural network is trained to learn the appropriate mapping between nonlinear input patterns and source bits. The simulation results provide some evidence that neural networks can learn the effect of nonlinear channels with memory and demodulate the output signal of...
Deep neural networks (DNN) have been successfully employed for the problem of monaural sound source separation achieving state-of-the-art results. In this paper, we propose using convolutional recurrent neural network (CRNN) architecture for tackling this problem. We focus on a scenario where low algorithmic delay (< 10 ms) is paramount, and relatively little training data is available. We show...
Artificial intelligence is widely used in image processing. Neural networks (NN) were successful used for solving complicated issues due to their capacity of generalization and learning from examples. In this paper some aspects of image compression using artificial neural networks are discussed. The network is used in the feedback loop of the visual servoing system, which aims to control a wheeled...
Deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures or otherwise composed of multiple non-linear transformations. In this paper, we present the results of testing neural networks architectures...
We present a deep learning architecture for learning fuzzy logic expressions. Our model uses an innovative, parameterized, differentiable activation function that can learn a number of logical operations by gradient descent. This activation function allows a neural network to determine the relationships between its input variables and provides insight into the logical significance of learned network...
In this work, we adopt the use of deep learning method for no-reference image quality assessment. With the development of deep neural networks technology, foundational and deep features of images could be captured without much prior knowledge. So a sparse autoencoder (SAE) was trained to express a 32 × 32 pixels image into a feature vector. Then the original images were cut into serial sub-images...
Stacked auto-encoder is mainly used for image classification and it can extract valid information from data through unsupervised pre-training and supervised fine-tuning. This paper is intended to improve the accuracy of image classification, we constructed a 6-layer stacked convolution neural network (CNN) based on stacked auto-encoders. The constructed CNN can extract effective features for image...
Dropout is a technique widely used for preventing overfitting while training deep neural networks. However, applying dropout to a neural network typically increases the training time. This paper proposes a different dropout approach called controlled dropout that improves training speed by dropping units in a column-wise or row-wise manner on the matrices. In controlled dropout, a network is trained...
A hybrid sampling technique is proposed by combining Complementary Fuzzy Support Vector Machine (CMTFSVM) and Synthetic Minority Oversampling Technique (SMOTE) for handling the imbalanced classification problem. The proposed technique uses an optimised membership function to enhance the classification performance and it is compared with three different classifiers. The experiments consisted of four...
Recently, a state-of-the-art algorithm, called deep deterministic policy gradient (DDPG), has achieved good performance in many continuous control tasks in the MuJoCo simulator. To further improve the efficiency of the experience replay mechanism in DDPG and thus speeding up the training process, in this paper, a prioritized experience replay method is proposed for the DDPG algorithm, where prioritized...
In this paper, we develop an end-to-end framework for text region proposal generation and text detection in the natural scene, which takes advantage of the efficient training strategies of the neural network. We make some technical changes over the SSD architectures, so that our method can handle the Chinese and English bilingual situation. Experiments show that our method exceeds the state-of-the-art...
A set of methods for estimating the parameters of the production process is proposed. This set includes expert assessments, Pareto analysis and elements of neural networks based artificial intelligence. The set forms up the methodology for assessing the state of the production process and forecasting its implementation. The sequence of application of individual methods is substantiated. Algorithms...
With the extensive application of machine learning algorithms in bioinformatics, more and more computer researchers are beginning to focus on this field. Polyadenylation of messenger RNA (mRNA) is one of the key steps of gene expression in eukaryotes, polyadenylation site marks the end of transcription, it is of great significance to explore prediction of the site of gene sequences encoding gene....
Deep learning has been proposed for soft sensor modeling in process industries. However, conventional deep neural network (DNN) is a static network and thereby can not embrace evident dynamics in processes. Motivated by nonlinear autoregressive with exogenous input (NARX) model and neural nets based dynamic modeling, a dynamic network called NARX-DNN is put forward by further utilizing historical...
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