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    • Open Access Article

      1 - A New Hybrid Method Based on Intelligent Algorithms for Intrusion Detection in SDN-IoT
      Zakaria Raeisi Fazlloah Adibnia Mahdi Yazdian
      Issue 1 , Vol. 22 , Spring 2024
      In recent years, the use of Internet of Things in societies has grown widely. On the other hand, a new technology called Software Defined Networks has been proposed to solve the challenges of the Internet of Things. The security problems in these Software Defined Networ More
      In recent years, the use of Internet of Things in societies has grown widely. On the other hand, a new technology called Software Defined Networks has been proposed to solve the challenges of the Internet of Things. The security problems in these Software Defined Networks and the Internet of Things have made SDN-IoT security one of the most important concerns. On the other hand, the use of intelligent algorithms has been an opportunity that these algorithms have been able to make significant progress in various cases such as image processing and disease diagnosis. Of course, intrusion detection systems for SDN-IoT environment still face the problem of high false alarm rate and low accuracy. In this article, a new hybrid method based on intelligent algorithms is proposed. The proposed method integrates the monitoring algorithms of frequent return gate and unsupervised k-means classifier in order to obtain suitable results in the field of intrusion detection. The simulation results show that the proposed method, by using the advantages of each of the integrated algorithms and covering each other's disadvantages, has more accuracy and a lower false alarm rate than other methods such as the Hamza method. Also, the proposed method has been able to reduce the false alarm rate to 1.1% and maintain the accuracy at around 99%. Manuscript profile

    • Open Access Article

      2 - Providing a Face Recognition System with an Optimal Selection of Features Based on the Cuckoo Optimization Algorithm
      Farnaz Hoseini Hamed Sepehrzadeh
      Issue 1 , Vol. 22 , Spring 2024
      Face recognition is a pattern recognition process that is specifically performed on faces. Face recognition has many applications in identifying credit cards, security systems, and other cases. Creating a face recognition system with high accuracy is a big challenge tha More
      Face recognition is a pattern recognition process that is specifically performed on faces. Face recognition has many applications in identifying credit cards, security systems, and other cases. Creating a face recognition system with high accuracy is a big challenge that has been the focus of various researchers in recent years. The feature extraction process and classification are two important issues in diagnosis systems that can play a significant role in increasing the accuracy of diagnosis. Considering this issue, in this study, taking into account the combined features and optimizing the cuckoo algorithm, a method to improve the accuracy of face recognition is proposed. In the presented method, seven features are extracted from the images in the database, and then by obtaining the feature vector, the steps related to feature selection are performed using the cuckoo algorithm. The proposed method has been implemented with MATLAB software and compared with other methods. The evaluation results show that the proposed method was able to perform the detection on the images of ORL and FDBB databases with 93.00% and 95.12% accuracy, respectively. The result obtained for this evaluation criterion has a higher value than other compared methods. Manuscript profile

    • Open Access Article

      3 - Ranking Improvement Using BERT
      shekoofe bostan Ali-Mohammad Zare-Bidoki Mohammad-Reza Pajoohan
      Issue 1 , Vol. 22 , Spring 2024
      In today's information age, efficient document ranking plays a crucial role in information retrieval systems. This article proposes a new approach to document ranking using embedding models, with a focus on the BERT language model to improve ranking results. The propose More
      In today's information age, efficient document ranking plays a crucial role in information retrieval systems. This article proposes a new approach to document ranking using embedding models, with a focus on the BERT language model to improve ranking results. The proposed approach uses vocabulary embedding methods to represent the semantic representations of user queries and document content. By converting textual data into semantic vectors, the relationships and similarities between queries and documents are evaluated under the proposed ranking relationships with lower cost. The proposed ranking relationships consider various factors to improve accuracy, including vocabulary embedding vectors, keyword location, and the impact of valuable words on ranking based on semantic vectors. Comparative experiments and analyses were conducted to evaluate the effectiveness of the proposed relationships. The empirical results demonstrate the effectiveness of the proposed approach in achieving higher accuracy compared to common ranking methods. These results indicate that the use of embedding models and their combination in proposed ranking relationships significantly improves ranking accuracy up to 0.87 in the best case. This study helps improve document ranking and demonstrates the potential of the BERT embedding model in improving ranking performance. Manuscript profile

    • Open Access Article

      4 - On the Behavior of Pre-trained Word Embedding Variants in Deep Headline Generation from Persian Texts
      Mohammad Ebrahim Shenassa Behrooz Minaei-Bidgoli
      Issue 1 , Vol. 22 , Spring 2024
      Inspired by sequence-to-sequence models for machine translation, deep-learning based summarization methods were presented. The summaries generated this way, are structurally more readable and usually convey the complete meaning to the reader. In these methods, embeddi More
      Inspired by sequence-to-sequence models for machine translation, deep-learning based summarization methods were presented. The summaries generated this way, are structurally more readable and usually convey the complete meaning to the reader. In these methods, embedding vectors are used for semantic representation, in which the weight of each word vector is learned according to its neighboring words from a large corpus. In static word embedding, the weight of the vectors is obtained by choosing a proximity window for each word. But in contextual ones like BERT, multilayer transformers are applied to calculate the weight of these vectors, which pay attention to all the words in the text. So far, several papers have shown that contextual word embedding are more successful than the other ones due to the ability of fine-tuning the weights to perform a specific natural language processing task. However, the performance of the initial weights of these vectors is not investigated for headline generation from Persian texts. In this paper, we will investigate the behavior of pre-trained word embedding variants without fine-tuning in deep headline generation from Persian texts. To train the headline generation model, "Elam Net" is used, which is a Persian corpus containing about 350 thousand pairs of abstracts and titles of scientific papers. The results show that the use of BERT model, even without fine-tuning its weights, is effective in improving the quality of generated Persian headlines, bringing the ROUGE-1 metric to 42%, which is better than the other pre-trained ones. Manuscript profile

    • Open Access Article

      5 - Emotion Recognition Based on EEG Signals Using Deep Learning Based on Bi-Directional Long Short-Term Memory and Attention Mechanism
      Seyyed Abed Hosseini M. Houshmand
      Issue 1 , Vol. 22 , Spring 2024
      This research deals with the recognition of emotions from EEG signals using deep learning based on bi-directional long short-term memory (LSTM) and attention mechanism. In this study, two SEED and DEAP databases are utilized for the emotion recognition. The SEED databas More
      This research deals with the recognition of emotions from EEG signals using deep learning based on bi-directional long short-term memory (LSTM) and attention mechanism. In this study, two SEED and DEAP databases are utilized for the emotion recognition. The SEED database includes EEG signals in 62 channels from 15 participants in three categories of positive, neutral, and negative emotions. The DEAP dataset includes EEG signals in 32 channels from 32 participants in two categories of valence and arousal. LSTM has shown its efficiency in extracting temporal information from long physiological signals. The innovations of this research include the use of a new loss function and Bayesian optimizer to find the initial learning rate. The accuracy of the proposed method for the classification of emotions in the SEED database is 96.72%. The accuracy of the proposed method for classifying emotions into two categories of valence and arousal is 94.9% and 97.1%, respectively. Finally, comparing the obtained results with recent research studies. Manuscript profile

    • Open Access Article

      6 - Comparison of Faster RCNN and RetinaNet for Car Recognition in Adverse Weather
      Yaser Jamshidi Raziyeh Sadat Okhovat
      Issue 1 , Vol. 22 , Spring 2024
      Vehicle detection and tracking plays an important role in self-driving cars and smart transportation systems. Adverse weather conditions, such as the heavy snow, fog, rain, dust, create dangerous limitations by reducing camera visibility and affect the performance of de More
      Vehicle detection and tracking plays an important role in self-driving cars and smart transportation systems. Adverse weather conditions, such as the heavy snow, fog, rain, dust, create dangerous limitations by reducing camera visibility and affect the performance of detection algorithms used in traffic management systems and autonomous cars. In this article, Faster RCNN deep object recognition network with ResNet50 core and RetinaNet network is used and the accuracy of these two networks for vehicle recognition in adverse weather is investigated. The used dataset is the DAWN file, which contains real-world images collected with different types of adverse weather conditions. The obtained results show that the presented method has increased the detection accuracy from 0.2% to 75% in the best case, and the highest increase in accuracy is related to rainy conditions. Manuscript profile

    • Open Access Article

      7 - Adaptive Acoustic Beamforming with Improved Differential Method
      Negar Sarshar Mehdi Bekrani
      Issue 1 , Vol. 22 , Spring 2024
      Differential beamformers exhibit effective performance in broadband applications, such as acoustic applications, but they have limited white noise gain. To address this limitation, this paper introduces an adaptive weighting-based algorithm designed to enhance the white More
      Differential beamformers exhibit effective performance in broadband applications, such as acoustic applications, but they have limited white noise gain. To address this limitation, this paper introduces an adaptive weighting-based algorithm designed to enhance the white noise gain of the differential beamformer by leveraging the minimum variance distortionless response (MVDR) beamforming technique. For this purpose, differential beamforming is implemented in two stages: in the first stage, the spatial difference of observations is obtained, and in the second stage, the beamformer is optimized. Subsequently, by calculating the coefficients and combining the differential and MVDR beamformers, the proposed adaptive beamformer is derived. In this beamformer, to construct the output signal, the contribution of the differential and MVDR methods is dynamically adjusted using an adaptive combination coefficient, which is a function of frequency, microphone inter-distance, target angle, and the number of microphones. The proposed beamformer, considering four microphones spaced 2 cm apart reveals a remarkable enhancement in white noise gain by 35 dB and SNR gain by 18 dB at a frequency of 1 kHz. Additionally, the proposed adaptive algorithm demonstrates a 3.5 dB improvement in directivity factor over its differential counterpart. Manuscript profile
    Most Viewed Articles

    • Open Access Article

      1 - Modeling and Reliability Evaluation of Magnetically Controlled Reactor based on the Markov Process Technique
      M. Haghshenas R. Hooshmand
      Issue 3 , Vol. 17 , Autumn 2019
      Controlled reactors as one of the flexible AC transmission systems play an important role in the availability and reliability of power systems.However, in the conventional reliability assessment of power systems, reactive power is considered only as a constraint for the More
      Controlled reactors as one of the flexible AC transmission systems play an important role in the availability and reliability of power systems.However, in the conventional reliability assessment of power systems, reactive power is considered only as a constraint for the network, and so far no precise model for assessing the reliability of reactors has been provided. In this paper, a new reliability model based on Markov process is proposed for a magnetically controlled reactor (MCR). In the modeling process, first the MCR structure is divided into two distinct parts, and then the extracted Markov models are combined based on frequency/duration technique.Since temperature changes play a significant role in changing the failure rate of electrical equipment, the effect of temperature changes in accordance with the MIL-217F standard has been considered in the proposed model and its impact on the probability of MCR operating modes has been evaluated. The simulation results have shown that in normal temperature conditions, the control system and at high temperatures, reactor windings can have the greatest impact on the availability of MCR. Comparison of reliability indices at different temperatures has shown that under different temperature conditions, different components will affect the availability of MCR. Therefore, in this condition, the measures needed to improve the reliability of the reactor can be different. This fact highlights the importance of considering the effect of operating temperature on reliability assessment as well as planning for preventive maintenance to improve the performance of reactive power sources. Manuscript profile

    • Open Access Article

      2 - Reactive Power Management in the Presence of Wind Turbine Considering Uncertainty of Load and Generation
      E. Moharamy S. Esmaeili
      Issue 3 , Vol. 13 , Autumn 2015
      Reactive power management is very important in power systems for the secure transmission of active power, especially when a part of system generation is provided by stochastic sources like wind energy. This paper presents a new algorithm for reactive power management in More
      Reactive power management is very important in power systems for the secure transmission of active power, especially when a part of system generation is provided by stochastic sources like wind energy. This paper presents a new algorithm for reactive power management in the presence of wind generators and considering the stochastic nature of these sources and load simultaneously .In this regard, the proposed probabilistic algorithm, minimizes the overall cost function of the system considering the cost of each of the reactive power sources including wind generators. Besides economic issues, the voltage stability margin, having sufficient reactive power reserve in each area of voltage control and considering transmission congestion probability as technical aspects of the planning, have been investigated .Another advantage of this method compared to the previous one, is using of doubly-fed induction generator (DFIG) and its capability in providing reactive power considering the constraints of grid side and rotor side converters. The proposed optimization algorithm uses a multi objective function with different weighting coefficients. This algorithm is applied to minimize total reactive power, cost and losses and maximize voltage stability margin and reactive power reserve, simultaneously, meanwhile the probabilistic nature of wind and load forecasting inaccuracy is considered in this algorithm. The proposed method is implemented on the IEEE 30-bus test system and the simulation results demonstrate the effectiveness of proposed algorithm in real conditions for PMSMs against internal faults, especially inter-turn faults. Manuscript profile

    • Open Access Article

      3 - Modeling of K-250 Compressor Using NARX and Hierarchical Fuzzy Model
      Adel Khosravi Abbas Chatraei G. Shahgholian Seyed-Mohamad Kargar
      Issue 3 , Vol. 18 , Autumn 2020
      Due to the increasing use of compressors in the industry, it is important to determine a mathematical model for the compressor to design a control system, analysis and simulation of the computer. Also, in recent years, smart modeling such as neural network and fuzzy net More
      Due to the increasing use of compressors in the industry, it is important to determine a mathematical model for the compressor to design a control system, analysis and simulation of the computer. Also, in recent years, smart modeling such as neural network and fuzzy network have been considered by researchers for their more realistic performance, and their types have been used for modeling. Smart methods have high capability to communicate between input and output data. In this paper, modeling of K-250 compressor at Isfahan smelter company based on smart models of fuzzy neural network is presented. The Nonlinear Auto Regressive With exogenous input (Narx) and hierarchical fuzzy network are presented. For modeling, the system has been tested and the input and output data of the compressor using compressor sensors and image processing are used to convert the data into the required data in the modeling, then the above algorithms of the compressor model will be achieved with the help of software, MATLAB. The results of modeling Which NARX performed better than hierarchical fuzzy. Among the two models presented in this paper, the NARX model shows a better response than the hierarchical fuzzy network in all cases and in all aspects of the performance criteria. Manuscript profile

    • Open Access Article

      4 - Design and Analysis of a Novel Robust and Fast Sliding-Mode Control with Multi-Slope Sliding Surface for Single-Phase Three Level NPC Inverters under Different Loads and Reduce the Output THD
      B. Khajeh-Shalaly G. Shahgholian
      Issue 2 , Vol. 15 , Summer 2017
      In this paper control structure with robust performance in presence of parametric uncertainties of the converter in order to improve pure sinusoidal inverter in whole functional and loading conditions is rendered. The controller guarantees fast and accurate behavior of More
      In this paper control structure with robust performance in presence of parametric uncertainties of the converter in order to improve pure sinusoidal inverter in whole functional and loading conditions is rendered. The controller guarantees fast and accurate behavior of the converter in order to increase the output voltage quality and reduce output harmonics. This controller by sliding performance and utilizing output voltage and capacitor current used in the control process, not only has exact output voltage tracking from reference but also has ability to reject the periodic disturbances due to loading. Also, it guides error states to zero rapidly and makes transient states of the converter as well as possible at error moments that is the same high spikes and loads in output current. Another characteristic of the proposed controller is, improved stability region under wide ranges of loading in different conditions. Accuracy of proposed controller on a single-phase three level NPC inverter which has high sensitivity in control in order to increase quality, decrease harmonics and THD output has been compared with a single-slope sliding mode controller with the sane loading conditions and reference. The simulations results are obtained by MATLAB. Manuscript profile

    • Open Access Article

      5 - Close Loop Identification for Combustion System by Recurrent Adaptive Neuro-Fuzzy Inference System and Network with Exogenous Inputs
      E. Aghadavoodi G. Shahgholian
      Issue 3 , Vol. 16 , Autumn 2018
      Boiler-turbine is a multi-variable and complicated system in steam power plants including combustion, temperature and drum water level. Selecting control loops as a unique loop in order to identify and control the boiler as a whole unit is a difficult and complicated ta More
      Boiler-turbine is a multi-variable and complicated system in steam power plants including combustion, temperature and drum water level. Selecting control loops as a unique loop in order to identify and control the boiler as a whole unit is a difficult and complicated task, because of nonlinear time variant dynamic characteristics of the boiler. It is necessary to identify each control group in order to accomplish a realistic and effective model, appropriate for designing an efficient controller. Both the effective and efficient performance of the identified model during the load change is of major importance. Here, not all parts of the system should be considered as a unit part, if determining and effective and realistic model is sought. The combustion loop of the 320 MW steam power plant of Islam Abad, Isfahan is the subject. Due to the sensitivity and complexity of the system, with respect to its nonlinear and closed loop characteristics, the identification of the system is conducted through intelligent procedures like recurrent adaptive neuro-fuzzy inference system (RANFIS) and nonlinear autoregressive model with exogenous input (NARX). The comparisons of the findings with actual data collected from the plant are presented and the accuracy of the procedures is determined. Manuscript profile

    • Open Access Article

      6 - Proposing a Density-Based Clustering Algorithm with Ability to Discover Multi-Density Clusters in Spatial Databases
      A. Zadedehbalaei A. Bagheri H. Afshar
      Issue 3 , Vol. 15 , Autumn 2017
      Clustering is one of the important techniques for knowledge discovery in spatial databases. density-based clustering algorithms are one of the main clustering methods in data mining. DBSCAN which is the base of density-based clustering algorithms, besides its benefits s More
      Clustering is one of the important techniques for knowledge discovery in spatial databases. density-based clustering algorithms are one of the main clustering methods in data mining. DBSCAN which is the base of density-based clustering algorithms, besides its benefits suffers from some issues such as difficulty in determining appropriate values for input parameters and inability to detect clusters with different densities. In this paper, we introduce a new clustering algorithm which unlike DBSCAN algorithm, can detect clusters with different densities. This algorithm also detects nested clusters and clusters sticking together. The idea of the proposed algorithm is as follows. First, we detect the different densities of the dataset by using a technique and Eps parameter is computed for each density. Then DBSCAN algorithm is adapted with the computed parameters to apply on the dataset. The experimental results which are obtained by running the suggested algorithm on standard and synthetic datasets by using well-known clustering assessment criteria are compared to the results of DBSCAN algorithm and some of its variants including VDBSCAN, VMDBSCAN, LDBSCAN, DVBSCAN and MDDBSCAN. All these algorithms have been introduced to solve the problem of multi-density data sets. The results show that the suggested algorithm has higher accuracy and lower error rate in comparison to the other algorithms. Manuscript profile

    • Open Access Article

      7 - Automatic Reference Image Selecting for Histogram Matching in Image Enhancement
      N. Samadiani H. Hassanpour
      Issue 2 , Vol. 13 , Summer 2015
      In this paper, a method is proposed to automatically select reference image in histogram matching. Histogram matching is one of the simplest spatial image enhancement methods which improves contrast of the initial image based on histogram of the reference image. In the More
      In this paper, a method is proposed to automatically select reference image in histogram matching. Histogram matching is one of the simplest spatial image enhancement methods which improves contrast of the initial image based on histogram of the reference image. In the conventional histogram matching methods, user should perform several experiments on various images to find a suitable reference image. This paper presents a new method to automatically select the reference image. In this method, images are converted from RGB to HSV, and the illumination (V) components are considered to select the reference image. The appropriate reference image is selected using a similarity measure via measuring the similarity between the histograms of the initial image and histograms of the images in the data base. Indeed, an image with similar histogram to the histogram of the original images is more appropriate to choose as the reference image for histogram matching. Results in this research indicate superiority of the proposed approach, compared to other existing approaches, in image enhancement via histogram matching. In addition, the user would have no concern in selecting an appropriate reference image for histogram matching in the proposed approach. This approach is applicable to both RGB and gray scale images. Manuscript profile

    • Open Access Article

      8 - PLAER: Penalty Base Learning Automata for Energy Aware Routing in WSN
      M. Parvizi Omran A. Moeni H. Haj Seyyed Javadi
      Issue 4 , Vol. 12 , Winter 2015
      Sensors in WSN work with batteries that have limited energy capacity. Therefore, reduction in power consumption is a very important issue. In this paper, we present a new routing algorithm to reduce power consumption in wireless sensor networks. This algorithm deploys L More
      Sensors in WSN work with batteries that have limited energy capacity. Therefore, reduction in power consumption is a very important issue. In this paper, we present a new routing algorithm to reduce power consumption in wireless sensor networks. This algorithm deploys Learning automata in each node to find a suitable path for routing data packets. In order to aim this goal the algorithm uses penalty based approach in learning automata and considers energy level of nodes and latency of packet delivery as well. Performance of our new developed algorithm has been compared with LABER and BEAR protocols in OMNET++ simulator. Simulation results show that, in a network with static nodes, energy consumption and control packets reduce significantly and network lifetime increases in comparison with two other protocols. Manuscript profile

    • Open Access Article

      9 - Design and Implementation of an IGBT Gate Driver with Necessary Protections and SMD Devices
      M. Fazeli S. A. Abrishamifar
      Issue 1 , Vol. 4 , Spring_Summer 2006
      The Gate drivers in modern power converters which use the power IGBT must be provide several main operations such as electrical isolation, current amplifying, and protection against overcurrent and overvoltage conditions. This paper describes such a new circuit which is More
      The Gate drivers in modern power converters which use the power IGBT must be provide several main operations such as electrical isolation, current amplifying, and protection against overcurrent and overvoltage conditions. This paper describes such a new circuit which is made using SMD devices suitable for driving the high and medium power IGBTs. This driver includes an isolated switching power supply, buffer circuits, and several protection circuits. It can operate by an input signal at TTL level and %50 duty cycle and is able to work up to 6A peak current. Manuscript profile

    • Open Access Article

      10 - Determination of Available Transfer Capability by Combined Method of Newton-Raphson-Seydel and Holomorphic Load Flow with Improved Matrix Calculations
      Mostafa Eidiani
      Issue 1 , Vol. 21 , Spring 2023
      This paper first demonstrates that high direct current lines will undoubtedly be the backbone of the future transmission network. The Newton Raphson Seydel alternating load flow equations are then combined with the direct current line equations. This paper employed matr More
      This paper first demonstrates that high direct current lines will undoubtedly be the backbone of the future transmission network. The Newton Raphson Seydel alternating load flow equations are then combined with the direct current line equations. This paper employed matrix techniques to increase the speed of solving problems as the dimensions of the equations get larger. Furthermore, the holomorphic load flow does not require an initial estimate to run the load flow, and if a solution exists, the precise answer is calculated. The initial guess of Newton Raphson Seydel was calculated using this approach. In this paper, we describe a novel approach that can compute the available transfer capability in small and large networks with sufficient accuracy and speed by combining these methods. The simulation in this paper uses five networks: 39 IEEE buses, 118 IEEE buses, 300 IEEE buses, 145 Iowa state buses, and 1153 East Iran buses network. In addition, four approaches were employed for comparison: continuous power flow, the general minimum residual method, Newton Raphson Seydel classical method, and the standard holomorphic power flow method. The results of the simulations suggest that the proposed strategy is acceptable. Manuscript profile
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    M. Ehsan (دانشگاه صنعتی شريف) R. Jalili (دانشگاه صنعتی شريف) Abdolhosein Rezaei (دانشگاه علم و فرهنگ) M. H. Savoji (دانشگاه شهید بهشتی) H. Seifi (دانشگاه تربیت مدرس) Mohammad Javad Shayeganfard (دانشگاه علم و فرهنگ) M. Shafiee (دانشگاه صنعتی امير کبير) Hamid Reza Sadegh Mohammadi (پژوهشکده برق جهاد دانشگاهی) A. Khaki Sedigh (دانشگاه صنعتی خواجه نصیرالدين طوسی) M. R. Aref (دانشگاه صنعتی شريف) M. Fathi (دانشگاه علم و صنعت ايران) M. K. Moravvej Farshi (دانشگاه تربيت مدرس)
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    Last Update 7/22/2024