شبکه عصبی

Open Access Article

1 - Electrical Islanding Detection in Electrical Distribution Networks with Distributed Generation Using Discrete Wavelet Transform and Artificial Neural Network
M. Heidari Orejloo S. Gh. Seifossadat M. Razaz

20.1001.1.16823745.1392.11.1.3.4

In this paper a new algorithm is provided for detecting of electrical islands, based on analysis of transient signals using discrete wavelet transform (DWT) and artificial neural network (ANN). The neural network is taught for Classification of events to the "islands" o More

In this paper a new algorithm is provided for detecting of electrical islands, based on analysis of transient signals using discrete wavelet transform (DWT) and artificial neural network (ANN). The neural network is taught for Classification of events to the "islands" or "non-islands". Needed features for classification are extracted by DWT of DG transient voltage signal. DIgSILENT, MATLAB and WEKA softwares are used for simulation. Proposed method is tested on a CIGRE medium voltage distribution system with two different types of DGs. The final method is chosen from among 162 relay projects with respect to different criteria, including accuracy, speed, simplicity and cost efficiency is the best. With The analysis done in the best relay selection for DGs, the voltage signal, the mother wavelet db4 and seventh level wavelet transform are used. Simulation results show that this method in compared with existing methods, can detect the electrical islands, with a shorter time and higher accuracy. Manuscript profile

Open Access Article

2 - Application of Wide-Area Synchrophasor Measurement System to Alleviate Blackouts by Rotor Angle Instability
S. Kiarostami S. Kiarostami

20.1001.1.16823745.1395.14.2.7.1

In this paper, a Wide-Area protection system to deal with rotor angle instabilities is proposed. Firstly, a system blackout model is developed and secondly the extreme contingencies that lead to large blackouts are extracted. Initiating events that ultimately lead to ro More

In this paper, a Wide-Area protection system to deal with rotor angle instabilities is proposed. Firstly, a system blackout model is developed and secondly the extreme contingencies that lead to large blackouts are extracted. Initiating events that ultimately lead to rotor angle instabilities are determined by artificial neural network (ANN). Coherent generators are detected by an algorithm using the data presented by phasor measurement units (PMUs). Based on identification of coherent generators, the power system is split into stable islands by disconnecting the weak interconnecting lines and load shedding. The performance of the proposed strategy is verified by simulations on the IEEE 39-bus sample power system. Manuscript profile

Open Access Article

3 - Human Action Recognition in Still Image of Human Pose using Multi-Stream neural Network
Roghayeh Yousefi K. Faez

20.1001.1.16823745.1399.18.3.8.2

Today, human action recognition in still images has become one of the active topics in computer vision and pattern recognition. The focus is on identifying human action or behavior in a single static image. Unlike the traditional methods that use videos or a sequence of More

Today, human action recognition in still images has become one of the active topics in computer vision and pattern recognition. The focus is on identifying human action or behavior in a single static image. Unlike the traditional methods that use videos or a sequence of images for human action recognition, still images do not involve temporal information. Therefore, still image-based action recognition is more challenging compared to video-based recognition. Given the importance of motion information in action recognition, the Im2flow method has been used to estimate motion information from a static image. To do this, three deep neural networks are combined together, called a three-stream neural network. The proposed structure of this paper, namely the three-stream network, stemmed from the combination of three deep neural networks. The first, second and third networks are trained based on the raw color image, the optical flow predicted by the image, and the human pose obtained in the image, respectively. In other words, in this study, in addition to the predicted spatial and temporal information, the information on human pose is also used for human action recognition due to its importance in recognition performance. Results revealed that the introduced three-stream neural network can improve the accuracy of human action recognition. The accuracy of the proposed method on Willow7 action, Pascal voc2012, and Stanford10 data sets were 91.8%, 91.02%, and 96.97%, respectively, which indicates the promising performance of the introduced method compared to state-of-the-art performance. Manuscript profile

Open Access Article

4 - Detecting Human Activities Based on Motion Sensors in IOT Using Deep Learning
Abbas Mirzaei fatemeh faraji

20.1001.1.16823745.1400.19.4.10.3

Control of areas and locations and motion sensors in the Internet of Things requires continuous control to detect human activities in different situations, which is an important challenge, including manpower and human error. Permanent human control of IoT motion sensors More

Control of areas and locations and motion sensors in the Internet of Things requires continuous control to detect human activities in different situations, which is an important challenge, including manpower and human error. Permanent human control of IoT motion sensors also seems impossible. The IoT is more than just a simple connection between devices and systems. IoT information sensors and systems help companies get a better view of system performance. This study presents a method based on deep learning and a 30-layer DNN neural network for detecting human activity on the Fordham University Activity Diagnostic Data Set. The data set contains more than 1 million lines in six classes to detect IoT activity. The proposed model had almost 90% and an error rate of 0.22 in the evaluation criteria, which indicates the good performance of deep learning in activity recognition. Manuscript profile

Open Access Article

5 - An Intelligent Vision System for Automatic Forest Fire Surveillance
Mohammad Sadegh Kayhanpanah Behrooz Koohestani

20.1001.1.16823745.1401.20.1.3.7

Fighting forest fires to avoid their potential dangers as well as protect natural resources is a challenge for researchers. The goal of this research is to identify the features of fire and smoke from the unmanned aerial vehicle (UAV) visual images for classification, o More

Fighting forest fires to avoid their potential dangers as well as protect natural resources is a challenge for researchers. The goal of this research is to identify the features of fire and smoke from the unmanned aerial vehicle (UAV) visual images for classification, object detection, and image segmentation. Because forests are highly complex and nonstructured environments, the use of the vision system is still having problems such as the analogues of flame characteristics to sunlight, plants, and animals, or the smoke blocking the images of the fire, which causes false alarms. The proposed method in this research is the use of convolutional neural networks (CNNs) as a deep learning method that can automatically extract or generate features in different layers. First, we collect data and increase them according to data augmentation methods, and then, the use of a 12-layer network for classification as well as transfer learning method for segmentation of images is proposed. The results show that the data augmentation method used due to resizing and processing the input images to the network to prevent the drastic reduction of the features in the original images and also the CNNs used can extract the fire and smoke features in the images well and finally detect and localize them. Manuscript profile

Open Access Article

6 - A POI Recommendation Model According to the Behavior Pattern of Users Based on Friends List Using Deep Learning
sadaf safavi mehrdad jalali

20.1001.1.16823745.1400.19.2.16.5

The rapid growth of Location-based Social Networks (LBSNs) is a great opportunity to provide personalized recommendation services. An important task to recommend an accurate Point-of-Interests (POIs) to users, given the challenges of rich contexts and data sparsity, is More

The rapid growth of Location-based Social Networks (LBSNs) is a great opportunity to provide personalized recommendation services. An important task to recommend an accurate Point-of-Interests (POIs) to users, given the challenges of rich contexts and data sparsity, is to investigate numerous significant traits of users and POIs. In this work, a novel method is presented for POI recommendation to develop the accurate sequence of top-k POIs to users, which is a combination of convolutional neural network, clustering and friendship. To discover the likeness, we use the mean-shift clustering method and only consider the influence of the most similarities in pattern’s friendship, which has the greatest psychological and behavioral impact rather than all user’s friendship. The new framework of a convolutional neural network with 10 layers can predict the next suitable venues and then select the accurate places based on the shortest distance from the similar friend behavior pattern. This approach is appraised on two LBSN datasets, and the experimental results represent that our strategy has significant improvements over the state-of-the-art techniques for POI recommendation. Manuscript profile

Open Access Article

7 - A Step towards All-Optical Deep Neural Networks: Utilizing Nonlinear Optical Element
Aida Ebrahimi Dehghan Pour S. K.

20.1001.1.16823745.1401.20.3.2.0

In recent years, optical neural networks have received a lot of attention due to their high speed and low power consumption. However, these networks still have many limitations. One of these limitations is implementing their nonlinear layer. In this paper, the implement More

In recent years, optical neural networks have received a lot of attention due to their high speed and low power consumption. However, these networks still have many limitations. One of these limitations is implementing their nonlinear layer. In this paper, the implementation of nonlinear unit for an optical convolutional neural network is investigated, so that using this nonlinear unit, we can realize an all-optical convolutional neural network with the same accuracy as the electrical networks, while providing higher speed and lower power consumption. In this regard, first of all, different methods of implementing optical nonlinear unit are reviewed. Then, the impact of utilizing saturable absorber, as the nonlinear unit in different layers of CNN, on the network’s accuracy is investigated, and finally, a new and simple method is proposed to preserve the accuracy of the optical neural networks utilizing saturable absorber as the nonlinear activating function. Manuscript profile

Open Access Article

8 - Stock Price Movement Prediction Using Directed Graph Attention Network
Alireza Jafari Saman Haratizadeh

20.1001.1.16823745.1402.21.1.13.4

Prediction of the future behavior of the stock market has always attracted researchers' attention as an important challenge in the field of machine learning. In recent years deep learning methods have been successfully applied in this domain to improve prediction perfor More

Prediction of the future behavior of the stock market has always attracted researchers' attention as an important challenge in the field of machine learning. In recent years deep learning methods have been successfully applied in this domain to improve prediction performance. Previous studies have demonstrated that aggregating information from related stocks can improve the performance of prediction. However, the capacity of modeling the stocks relations as directed graphs and the power of sophisticated graph embedding techniques such as Graph Attention Networks have not been exploited so far for prediction in this domain. In this work, we introduce a framework called DeepNet that creates a directed graph representing how useful the data from each stock can be for improving the prediction accuracy of any other stocks. DeepNet then applies Graph Attention Network to extract a useful representation for each node by aggregating information from its neighbors, while the optimal amount of each neighbor's contribution is learned during the training phase. We have developed a novel Graph Attention Network model called DGAT that is able to define unequal contribution values for each pair of adjacent nodes in a directed graph. Our evaluation experiments on the Tehran Stock Exchange data show that the introduced prediction model outperforms the state-of-the-art baseline algorithms in terms of accuracy and MCC measures. Manuscript profile

Open Access Article

9 - Spam Detection in Twitter by Ensemble Learning Approach
Maryam Fasihi Mohammad Javad shayegan zahra hosieni zahra sejdeh

Today, social networks play a crucial role in disseminating information worldwide. Twitter is one of the most popular social networks, with 500 million tweets sent on a daily basis. The popularity of this network among users has led spammers to exploit it for distributi More

Today, social networks play a crucial role in disseminating information worldwide. Twitter is one of the most popular social networks, with 500 million tweets sent on a daily basis. The popularity of this network among users has led spammers to exploit it for distributing spam posts. This paper employs a combination of machine learning methods to identify spam at the tweet level. The proposed method utilizes a feature extraction framework in two stages. In the first stage, Stacked Autoencoder is used for feature extraction, and in the second stage, the extracted features from the last layer of Stacked Autoencoder are fed into the softmax layer for prediction. The proposed method is compared and evaluated against some popular methods on the Twitter Spam Detection corpus using accuracy, precision, recall, and F1-score metrics. The research results indicate that the proposed method achieves a detection of 78.1%. Overall, the proposed method, using the majority voting approach with a hard selection in ensemble learning, outperforms CNN, LSTM, and SCCL methods in identifying spam tweets with higher accuracy. Manuscript profile

Open Access Article

10 - Data-Driven Sliding Mode Control Based on Projection Recurrent Neural Network for HIV Infection: A Singular Value Approach
Ashkan Zarghami mehdi Siahi Fereidoun Nowshiravan Rahatabad

In the present study, drug treatment of HIV infection is investigated using a Data-Driven Sliding Mode Control (DDSMC) combined with a Projection Recurrent Neural Network (PRNN). The major objective is to establish the control law that eliminates the need for HIV infect More

In the present study, drug treatment of HIV infection is investigated using a Data-Driven Sliding Mode Control (DDSMC) combined with a Projection Recurrent Neural Network (PRNN). The major objective is to establish the control law that eliminates the need for HIV infection mathematical formulae and ensures that the physical limits of the actuator are reached. This is accomplished by creating the concepts of model-free adaptive control, in which the relation between input and output is described using local dynamic linearized models based on quasi-partial derivatives. To determine the DDSMC law, a performance index is first defined based on the fulfillment of a discrete-time exponential reaching condition. By turning this index into a quadratic programming problem, the dynamics of the PRNN are extracted based on projection theory. The closed-loop system is explicitly determined using the optimizer output equation and the closed-loop stability analysis is evaluated using the singular value approach. The simulation results reveal that the proposed algorithm has robust performance in conducting the state variables of HIV infection to the healthy equilibrium point in the face of model uncertainty and external disturbances when compared to one of the newest control techniques. Manuscript profile

Open Access Article

11 - Identification of Cancer-Causing Genes in Gene Network Using Feedforward Neural Network Architecture
مصطفی اخوان صفار abbas ali rezaee

Identifying the genes that initiate cancer or the cause of cancer is one of the important research topics in the field of oncology and bioinformatics. After the mutation occurs in the cancer-causing genes, they transfer it to other genes through protein-protein interact More

Identifying the genes that initiate cancer or the cause of cancer is one of the important research topics in the field of oncology and bioinformatics. After the mutation occurs in the cancer-causing genes, they transfer it to other genes through protein-protein interactions, and in this way, they cause cell dysfunction and the occurrence of disease and cancer. So far, various methods have been proposed to predict and classify cancer-causing genes. These methods mostly rely on genomic and transcriptomic data. Therefore, they have a low harmonic mean in the results. Research in this field continues to improve the accuracy of the results. Therefore, network-based methods and bioinformatics have come to the aid of this field. In this study, we proposed an approach that does not rely on mutation data and uses network methods for feature extraction and feedforward three-layer neural network for gene classification. For this purpose, the breast cancer transcriptional regulatory network was first constructed. Then, the different features of each gene were extracted as vectors. Finally, the obtained vectors were given to a feedforward neural network for classification. The obtained results show that the use of methods based on multilayer neural networks can improve the accuracy and harmonic mean and improve the performance compared to other computational methods. Manuscript profile

Open Access Article

12 - Modeling of Solar Power Plant Using a Neural Network Based on the Equivalent of a Single Diode
Ali Reza reisi Rohollah Abdollahi

<p style="padding-right: 30px; text-align: justify;"><!-- [if gte mso 9]><xml> <w:WordDocument> <w:View>Norma More

<p style="padding-right: 30px; text-align: justify;"></p> <p class="Abstract" style="padding-right: 30px; text-align: justify;"><span style="mso-bidi-font-weight: normal;">Various methods have been proposed for modeling solar panels, but modeling solar power plants using them is associated with challenges. In equivalent circuit-based methods, the modeling depends on factory data that changes over time. Modeling of voltage-current characteristic using intelligent methods such as neural network was less considered due to the low accuracy of modeling. In this article, a method independent of the manufacturer's data for modeling the solar power plant is presented, so that it is possible to accurately model the solar power plants that have been installed for several years. The proposed method consists of two steps, in the first step, open circuit voltage, maximum power point and short circuit current are modeled according to atmospheric conditions using neural network. In the second step, the unknown parameters of the equivalent circuit are determined by circuit analysis relations and using neural network outputs. Finally, to evaluate the proposed method, a 3-kW solar power plant was modeled, and the results show the appropriate accuracy of the proposed method for modeling the solar power plant.</span></p> <p style="padding-right: 90px; text-align: justify;"> </p> Manuscript profile

Open Access Article

13 - Nonlinear Fractional Intelligent Controller for Photovoltaic Inverters
Hadi Delavari Sara Arjmandpour

<p style="text-align: justify; padding-right: 30px;"><!-- [if gte mso 9]><xml> <w:WordDocument> <w:View>Norma More

<p style="text-align: justify; padding-right: 30px;"><span style="font-size: 10.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">At present, with the significant growth of energy consumption, increase of greenhouse gases and environmental pollutants, more attention is directed toward renewable energies. Renewable energies include geothermal, wind, photovoltaic energy and etc. Among the advantages of photovoltaic energy, its wide range and easy access, helping to preserve the environment, compatibility with distributed power networks, low noise, quick installation and lower cost compared to other energies can be noted. Important challenges facing photovoltaic systems are changing climatic conditions and parameters variation that affect the performance of the system. In this paper, to track the maximum power point in a photovoltaic system, a fuzzy fractional order sliding mode controller based on disturbance observer and uncertainty estimator using neural network is designed. The sliding mode control is used to reduce chattering, neural network to estimate the system uncertainties, fuzzy system to estimate the coefficient of the signum function in the control law and disturbance observer to approximate the disturbances in the system. Also, the stability of the system has been proven using the Lyapunov method. The simulation results of the photovoltaic system confirm the effectiveness of the proposed method and shows satisfactory performance.</span></p> Manuscript profile

Current Issue

Published Issues

Menu

Browse

List of Articles شبکه عصبی