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Open Access Article
1 - Designing a Secure Consensus Algorithm for Use in Blockchain
Hosein Badri Masumeh SafkhaniIssue 4 , Vol. 21 , Winter 2024Blockchain technology eliminates the need for a central authority. This system consists of a distributed ledger with a chain of blocks that records every network transaction. This ledger is replicated by every node in the network. We require a mechanism that provides co MoreBlockchain technology eliminates the need for a central authority. This system consists of a distributed ledger with a chain of blocks that records every network transaction. This ledger is replicated by every node in the network. We require a mechanism that provides consensus for the entire network, known as "consensus algorithm," in order for the state of this ledger to be the same for all nodes of the network at any given time. In this work, we will suggest a novel consensus algorithm that protects the blockchain platform from four common attacks. These attacks include the Sybil, Denial of Service, 51%, and Eclipse attacks. Due to its multiple control parameters, generic and all-purpose character, immunity to different attacks, and acceptable execution speed, our suggested algorithm can be used to build secure blockchain-based systems in a variety of applications. Manuscript profile -
Open Access Article
2 - A New Parallel Method to Verify the Packets Forwarding in SDN Networks
Rozbeh Beglari Hakem BeitollahiIssue 4 , Vol. 21 , Winter 2024The rise of Software-Defined Networking (SDN) has revolutionized network management, offering greater flexibility and programmability. However, ensuring the accuracy of packet forwarding remains paramount for maintaining network reliability and security in SDN environme MoreThe rise of Software-Defined Networking (SDN) has revolutionized network management, offering greater flexibility and programmability. However, ensuring the accuracy of packet forwarding remains paramount for maintaining network reliability and security in SDN environments. Unlike traditional IP networks, SDN separates the control plane from the data plane, creating new challenges for securing data transmission. Existing verification methods designed for IP networks often cannot be directly applied to SDN due to this architectural difference. To address the limitations of existing verification methods in SDN networks, new approaches are necessary. This research proposes a novel parallel method for verifying packet forwarding, building upon concepts from DYNAPFV. The proposed approach aims to overcome specific limitations of existing methods (including DYNAPFV), such as scalability issues, slow verification times. Simulations demonstrate significant improvements compared to DYNAPFV. The proposed parallel method achieves a 92% reduction in time required to identify malicious nodes within the network. The results also reveal a trade-off between security and verification time. As the probability of packet integrity confirmation increases from 0.8 to 0.99, system security strengthens, but the time to detect malicious switches also increases. Manuscript profile -
Open Access Article
3 - Machine Learning-Based Security Resource Allocation for Defending against Attacks in the Internet of Things
Nasim Navaei Vesal HakamiIssue 4 , Vol. 21 , Winter 2024Nowadays, the Internet of Things (IoT) has become the focus of security attacks due to the limitation of processing resources, heterogeneity, energy limitation in objects, and the lack of a single standard for implementing security mechanisms. In this article, a solutio MoreNowadays, the Internet of Things (IoT) has become the focus of security attacks due to the limitation of processing resources, heterogeneity, energy limitation in objects, and the lack of a single standard for implementing security mechanisms. In this article, a solution will be presented for the problem of security resources allocating to deal with attacks in the Internet of Things. Security Resource Allocation (SRA) problem in the IoT networks refers to the placement of the security resources in the IoT infrastructure. To solve this problem, it is mandatory to consider the dynamic nature of the communication environments and the uncertainty of the attackers' actions. In the traditional approaches for solving the SRA, the attacker works over based on his assumptions about the system conditions. Meanwhile, the defender collects the system's information with prior knowledge of the attacker's behavior and the targeted nodes. Unlike the mentioned traditional approaches, this research has adopted a realistic approach for the Dynamic Security Resources Allocation in the IoT to battle attackers with unknown behavior. In the stated problem, since there is a need to decide on deploying several security resources during the learning periods, the state space of the strategies is expressed in the combinatorial form. Also, the SRAIoT problem is defined as a combinatorial-adversarial multi-armed bandit problem. Since switching in the security resources has a high cost, in real scenarios, this cost is included in the utility function of the problem. Thus, the proposed framework considers the switching cost and the earned reward. The simulation results show a faster convergence of the weak regret criterion of the proposed algorithms than the basic combinatorial algorithm. In addition, in order to simulate the IoT network in a realistic context, the attack scenario has been simulated using the Cooja simulator. Manuscript profile -
Open Access Article
4 - Friendship Selection Based on Social Features in Social Internet of Things
Mohammad Mahdian S.Mojtaba MatinkhahIssue 4 , Vol. 21 , Winter 2024The Social Internet of Things (SIoT) network is the result of the union of the Social Network and the Internet of Things network; wherein, each object tries to use the services provided by its friends. In this network, to find the right friend in order to use the right MoreThe Social Internet of Things (SIoT) network is the result of the union of the Social Network and the Internet of Things network; wherein, each object tries to use the services provided by its friends. In this network, to find the right friend in order to use the right service is demanding. Great number of objects' friends, in classical algorithms, causes increasing the computational time and load of network navigation to find the right service with the help of friendly objects. In this article, in order to reduce the computational load and network navigation, it is proposed, firstly, to select the appropriate object friend from a heuristic approach; secondly, to use an adapted binary cuckoo optimization algorithm (AB-COA) which tries to select the appropriate friendly object to receive the service according to the maximum response capacity of each friendly object, and finally, adopting the Adamic-Adar local index (AA) with the interest degree centrality criterion so that it represents the characteristics of the common neighbors of the objects are involved in the friend selection. Finally, by executing the proposed algorithm on the Web-Stanford dataset, an average of 4.8 steps was obtained for reaching a service in the network, indicating the superiority of this algorithm over other algorithms. Manuscript profile -
Open Access Article
5 - Combination of Instance Selection and Data Augmentation Techniques for Imbalanced Data Classification
Parastoo Mohaghegh Samira Noferesti Mehri RajaeiIssue 4 , Vol. 21 , Winter 2024Mohaghegh, S. Noferesti*, and M. Rajaei Abstract: In the era of big data, automatic data analysis techniques such as data mining have been widely used for decision-making and have become very effective. Among data mining techniques, classification is a common method fo MoreMohaghegh, S. Noferesti*, and M. Rajaei Abstract: In the era of big data, automatic data analysis techniques such as data mining have been widely used for decision-making and have become very effective. Among data mining techniques, classification is a common method for decision making and prediction. Classification algorithms usually work well on balanced datasets. However, one of the challenges of the classification algorithms is how to correctly predicting the label of new samples based on learning on imbalanced datasets. In this type of dataset, the heterogeneous distribution of the data in different classes causes examples of the minority class to be ignored in the learning process, while this class is more important in some prediction problems. To deal with this issue, in this paper, an efficient method for balancing the imbalanced dataset is presented, which improves the accuracy of the machine learning algorithms to correct prediction of the class label of new samples. According to the evaluations, the proposed method has a better performance compared to other methods based on two common criteria in evaluating the classification of imbalanced datasets, namely "Balanced Accuracy" and "Specificity". Manuscript profile -
Open Access Article
6 - Spam Detection in Twitter by Ensemble Learning Approach
Maryam Fasihi Mohammad Javad shayegan zahra hosieni zahra sejdehIssue 4 , Vol. 21 , Winter 2024Today, 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 MoreToday, 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
7 - Optimization of Initial States for Adiabatic Quantum Computing in a Quantum Algorithm
Arash Karimkhani Amir Ghal’eIssue 4 , Vol. 21 , Winter 2024In any adiabatic quantum computation, there exist an initial state that must be used in the corresponding quantum algorithm. In this paper, the relation between an initial state and allowed energy level of an implemented generalized Deutsch’s algorithm is investigated. MoreIn any adiabatic quantum computation, there exist an initial state that must be used in the corresponding quantum algorithm. In this paper, the relation between an initial state and allowed energy level of an implemented generalized Deutsch’s algorithm is investigated. To study the generalized Deutsch’s algorithm, a compacted form for the output states of the algorithm is obtained. It has been shown that one can prepare the initial states in such a way that control the minimum of energy. By using numerical methods, the minimum values of allowed energy levels for the initial state are obtained. Also, to study the dynamics of the system is chosen. The corresponding Hamiltonian for the algorithm is obtained and it has been shown that one of the energy levels describes a binding state. Manuscript profile
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Open Access Article
1 - Modeling and Reliability Evaluation of Magnetically Controlled Reactor based on the Markov Process Technique
M. Haghshenas R. HooshmandIssue 3 , Vol. 17 , Autumn 2019Controlled 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 MoreControlled 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. EsmaeiliIssue 3 , Vol. 13 , Autumn 2015Reactive 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 MoreReactive 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 KargarIssue 3 , Vol. 18 , Autumn 2020Due 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 MoreDue 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. ShahgholianIssue 2 , Vol. 15 , Summer 2017In 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 MoreIn 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. ShahgholianIssue 3 , Vol. 16 , Autumn 2018Boiler-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 MoreBoiler-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. AfsharIssue 3 , Vol. 15 , Autumn 2017Clustering 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 MoreClustering 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. HassanpourIssue 2 , Vol. 13 , Summer 2015In 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 MoreIn 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 - Design and Implementation of an IGBT Gate Driver with Necessary Protections and SMD Devices
M. Fazeli S. A. AbrishamifarIssue 1 , Vol. 4 , Spring_Summer 2006The 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 MoreThe 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
9 - PLAER: Penalty Base Learning Automata for Energy Aware Routing in WSN
M. Parvizi Omran A. Moeni H. Haj Seyyed JavadiIssue 4 , Vol. 12 , Winter 2015Sensors 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 MoreSensors 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
10 - Determination of Available Transfer Capability by Combined Method of Newton-Raphson-Seydel and Holomorphic Load Flow with Improved Matrix Calculations
Mostafa EidianiIssue 1 , Vol. 21 , Spring 2023This 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 MoreThis 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
