The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Harmonics are usually analyzed through Fourier transform or wavelet transform, both of which have their shortcomings. Wavelet neural-network is a new algorithm that combines wavelet analysis with neural-network, suitable for processing signals with time-varying frequency domain characters. To analyze the third and fifth harmonics, a wavelet network structure was constructed, training algorithm described...
Price forecasting has become essential tool in deregulated electricity market. It is used by utility operators for bidding in the competitive market to increase their profits and services. The models for electricity price forecasting can be mainly categorized into (i) Statistical models, (ii) Artificial Intelligence models & (iii) Hybrid models. AI based models, i.e., ANN have gained popularity...
Digital fundus photographs are often used to provide clinical diagnostic information about several pathologies such as diabetes, glaucoma, macular degeneration and vascular and neurologic disorders. To allow a precise analysis, digital fundus image quality should be assessed to evaluate if minimum requirements are present. Focus is one of the causes of low image quality. This paper describes a method...
Classification of signals acquired by condition monitoring systems for automotive application is becoming increasingly important. The work presented in this paper is motivated by a real-life classification challenge organized by Ford. Data samples from an automotive subsystem were collected. A classifier is designed to robustly isolate the different types of problems, by analyzing the acquired signals...
The combination of wavelet theory and feedforward artificial neural networks has resulted in wavelet neural networks or wavenets (WNNs). In these networks, the activation functions are described by discrete wavelet functions. Due to the promising properties of time-frequency localization and multi-resolution signal processing of the wavelet transform combined with the approximation capability of artificial...
High impedance fault (HIF) is very common problem and complex phenomena, and because of its distinctive characteristic is considered as riskiness for public safety and human. Therefore, the detection and protection of such faults still remain a topic of research and challenging of protection engineers. In this paper, a new model of (HIF) is introduced and tested with applying of new hybrid algorithm...
Breast cancer is the leading cause of cancer mortality among women and early diagnosis with proper treatment is the key to survival. Women who practice regular breast self-examination are the ones most likely to detect early abnormalities in their breast. However, studies have shown that most women performing BSE do not carry out the procedure efficiently. This paper presents a method for BSE procedure...
A data-based fault diagnosis method is applied to fault diagnosis of traction converter in this paper. The wavelet transform is used to extract fault characteristics and support vector machine (SVM) is used to diagnose faults. The pros and cons of SVM and radial basis neural network (RBF NN) in fault model classification are also compared follow behind. The simulation results show that, SVM has a...
Financial Stock prediction presents a challenging task that attracts great interest from researchers and investors because of potential substantial rewards. However, the field still requires a more precise process. This paper presents an integrated system formed by data preprocessing techniques and a hybrid algorithm combining Artificial Bee Colony (ABC) and Back Propagation (BP) algorithms to train...
This paper presents a new diagnosis method for classifying current waveform events that are related to a variety of induction machine faults. The method is composed of two sequential processes: feature extraction and classification. The essence of the feature extraction is to project a faulty machine signal onto a low dimension time-frequency representation (TFR), which is deliberately designed for...
This paper presents a novel approach to optimize pattern recognition system using genetic algorithm (GA) to identify the type of hand motion employing artificial neural networks (ANNs) with high performance and accuracy suited for practical implementations. To achieve this approach, electromyographic (EMG) signals were obtained from sixteen locations on the forearm of six subjects in ten hand motion...
A forecasting model for gas emission based on wavelet neural network is proposed in this paper. In the model, wavelet neutral network (WNN) is applied to the forecasting with gradient descent and amended by validity of iteration training algorithm. Compared with back-propagation neural networks, forecasting of the model has advantages of faster convergence and more accurate. Simulation results have...
LPR (License Plate Recognition) is a foundation component of modern transportation management systems. It uses a set of computer image-processing technologies to identify vehicle by its license plate. Character recognition is the core of LPR, which is essentially a multi-classification problem. The challenge is how to recognize every character of the license plate accurately and rapidly in case of...
Improving the diversity of Neural Network Ensembles (NNE) plays an important role in creating robust classification systems in many fields. Several methods have been proposed in the literature to create such diversity using different sets of classifiers or using different portions of training/feature sets. Neural networks are often used as base classifiers in multiple classifier systems as they adapt...
This paper suggests a methodology for segmentation of masses in digital mammograms. The masses are distinguished from other breast tissue by its homogeneous and differentiated density in relation to other breast tissues. The segmentation strategy is based on the assessment of density using multiscale wavelet transform. The density data obtained by processing with wavelet are used to train multilayer...
The back-propagation algorithm has been used widely as a learning algorithm in a feed-forward multilayer neural network. In this study, fault detection was carried out using the information of the arc current. After collecting the actual data, wavelet transformation were adopted in order to obtain the sideband or detail value characteristics under healthy and various faulty operating conditions. The...
This work presents how to improve an algorithm based on artificial neural network (ANN) for microwave filter tuning. Sets of ANN learning vectors which contain scattering filter characteristics and corresponding tuning elements deviations are used in the concept. The main idea is to transform filter characteristics to wavelet Daubechies D4 representation before ANN training. Experimental results have...
Due to the highly complex dynamics of hydraulic generator unit, it is hard to develop an accurate analytical expression of the dynamic model, a new adaptive control algorithm based on the learning characteristic of neural network and the function approximation ability of the wavelet is presented in this paper. The control system consists of two wavelet networks, one realizes active identification...
Considering about the fault features of traction machine for lifts, the basic characteristics of faults types are analyzed. By detecting vibration signals from vibration sensors, uses wavelet packet to decompose fault signal, extracts the signal characteristics of 8 frequency components from the low-frequency to high frequency in the third layer. The 8 obtained eigenvalues as the fault signals are...
Significance of equipment fault diagnosis is mainly reflected in lower failure rate; lower maintenance costs reduce maintenance time, increase operating time. Wavelet network is the perfect combination of the theory of wavelet analysis and the theory artificial neural network; it is compatible with the superiority of the wavelet and neural networks. In this paper, the wavelet neural network based...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.