Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1159
Operations and Ranking Methods for Intuitionistic Fuzzy Numbers, a Review and New Methods
Статья научная
Intuitionistic Fuzzy Numbers (IFNs) transfer more information than fuzzy numbers do in uncertain situations. It is caused that many others tried to define methods for ranking of IFNs and arithmetic operations on them, which are used in practical applications of IFNs such as decision making. Arithmetic operators on IFNs changed membership and non-membership degrees. The resulted degrees have important interpretations in real application of IFNs. In this paper, we will first review the existing methods for ranking and arithmetic operations on several representations of IFNs. Then, we will propose a new method based on arithmetic mean and geometric mean to compute membership and non-membership degrees of resulted IFN from arithmetic operations on IFNs. It is caused that the resulted degrees don't change monotonousness and be closer to reality. Furthermore, a new method for ranking of IFNs will be proposed. Finally, the proposed methods are used in the numerical examples, compared to some other existing methods.
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Opinion score mining: an algorithmic approach
Статья научная
Opinions are used to express views and reviews are used to provide information about how a product is perceived. People contributions lie in posting text messages in the form their opinions and emotions which may be based on different topics such as movie, book, product, and politics and so on. The reviews available online can be available in thousands, so making the right decision to select a product becomes a very tedious task. Several research works has been proposed in the past but they were limited to certain issues discussed in this paper. The reviews are collected which periodically updates itself using crawler discussed in our previous work. Further after applying certain pre-processing tasks in order to filter reviews and remove unwanted tokens, the sentiments are classified according to the novel unsupervised algorithm proposed. Our algorithm does not require annotated training data and is adequate to sufficiently classify the raw text into each domain and it is applicable enough to categorize complex cases of reviews as well. Therefore, we propose a novel unsupervised algorithm for categorizing sentiments into positive, negative and neutral category. The accuracy of the designed algorithm is evaluated using the standard datasets like IRIS, MTCARS, and HAR.
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Optimal Control Approach for Solving Linear Volterra Integral Equations
Статья научная
In this paper we present a new approach for linear Volterra integral equations that is based on optimal control theory. Some optimal control problems corresponding Volterra integral equation be introduced which we solve these problems by discretization methods and linear programming approaches. Finally, some examples are given to show the efficiency of approach.
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Optimal Control of Switched Systems based on Bezier Control Points
Статья научная
This paper presents a new approach for solving optimal control problems for switched systems. We focus on problems in which a pre-specified sequence of active subsystems is given. For such problems, we need to seek both the optimal switching instants and the optimal continuous inputs. A Bezier control points method is applied for solving an optimal control problem which is supervised by a switched dynamic system. Two steps of approximation exist here. First, the time interval is divided into k sub-intervals. Second, the trajectory and control functions are approximatedby Bezier curves in each subinterval. Bezier curves have been considered as piecewise polynomials of degree n, then they will be determined by n+1 control points on any subinterval. The optimal control problem is there by converted into a nonlinear programming problem (NLP), which can be solved by known algorithms. However in this paper the MATLAB optimization routine FMINCON is used for solving resulting NLP.
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Статья научная
Optimal coordination of Inverse Definite Minimum Time (IDMT) direction overcurrent relays in the power systems in the presence of multiple Thyristor Controller Series Capacitor (TCSC) on inductive and capacitive operation mode on meshed power system is studied in this paper. The coordination problem is formulated as a non-linear constrained mono-objective optimization problem. The objective function of this optimization problem is the minimization of the operation time (T) of the associated relays in the systems, and the decision variables are: the time dial setting (TDS) and the pickup current setting (IP) of each relay. To solve this complex non linear optimization problem, a variant of evolutionary optimization techniques named Biogeography Based Optimization (BBO) is used. The proposed algorithm is validated on IEEE 14-bus transmission network test system considering various scenarios. The obtained results show a high efficiency of the proposed method to solve such complex optimization problem, in such a way the relays coordination is guaranteed for all simulation scenarios with minimum operating time. The results of new relay setting are compared to other optimization algorithms.
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Статья научная
Presence of distributed generation (DG) in distribution systems has significant impacts on the operational characteristics of these systems, also using capacitor for reactive compensation and loss reduction is so common. Injected harmonic currents from non-linear loads into distribution system distort all of voltages and currents and must be considered when placing the capacitor banks so that the resonance will not occur. In this paper discrete particle swarm optimization (DPSO) approach is used for the optimal placement and sizing of distributed generations and capacitors in distribution systems for simultaneous voltage profile improvement, loss and total harmonic distortion (THD) reduction. There is a term in the objective function which prevents harmonic resonance between capacitor reactance and system reactance. Constraints include voltage limit, voltage THD, number/ size of capacitors and generators. For evaluating the proposed algorithm, the IEEE 33-bus test system is modified and employed.
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Статья научная
Presence of distributed generation (DG) in distribution systems has significant impacts on the operational characteristics of these systems, also using capacitor for reactive compensation and loss reduction is so common. Injected harmonic currents from non-linear loads into distribution system distort all of voltages and currents and must be considered when placing the capacitor banks so that the resonance will not occur. Distributed Generation is often connected to the network via power-electronic interfaces for a proper coupling with the distribution networks. Inverters are capable of producing harmonic components and can be used as ancillary services for reducing harmonics by designing of a proper controlling system. In this paper discrete particle swarm optimization (DPSO) approach is used for the optimal placement and sizing of distributed generations and capacitors in distorted distribution systems for simultaneous voltage profile improvement, loss and total harmonic distortion (THD) reduction. Constraints include voltage limit, voltage THD, number/ size of capacitors and generators. For evaluating the proposed algorithm, the IEEE 33-bus test system is modified and employed.
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Статья научная
Saving energy through the minimization of power losses in a distribution system is a key activity for efficient operation. Distributed Generation (DG) is one of the most efficient approaches to minimize losses. With increase in installation of Electric Vehicle Charging Stations (EVCSs) for Electrical Vehicles (EVs) in larger scale, optimal planning of EVCSs becomes a major challenge for distribution system operator. With increased EV load penetration in the electricity system, generation-demand mismatch and power losses increases. This results in poor voltage level, and deterioration in voltage stability margin. To mitigate the adverse impacts of increasing EV load penetration on Radial Distribution Systems (RDS), it is essential to integrate EVCSs at appropriate locations. The EVs integration into smart distribution systems involves Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) in charging and discharging modes of operation respectively for exchange of power with the grid thus resulting in energy management. The inappropriate planning of EVCSs causes a negative impact on the distribution system such as voltage deviation and an increase in power losses. In order to minimize this, DG units are integrated with EVCSs. The DGs assist in keeping the voltage profile within limitations, resulting in reduced power flows and losses, thereby enhancing power quality and reliability. Therefore, the DGs should be optimally allocated and sized along with the EVCS to avoid problems such as protection, voltage rise, and reverse power flow problems. This paper showcases a method to minimize losses using optimal location and sizing of multiple DGs and EVCS operating in G2V and V2G modes. The sizing and location of different types of DG units including renewables and non-renewables along with EV charging station is proposed in this study. This methodology overall reduces the power losses and also improves voltages of the network. The implementation is done by using the Simultaneous Particle Swarm Optimization technique (PSO) for IEEE 15, 33, 69 and 85 bus systems. The results indicate that the proposed optimization technique improves efficiency and performance of the system by optimal planning and operation of both DGs and EVs.
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Optimal Power Flow Solution using Efficient Sine Cosine Optimization Algorithm
Статья научная
The problem encountered in most metaheuristic methods is the choice of the good control parameters of the algorithm. That is the objective of this work by using an efficient sine cosine algorithm (ESCA) in optimal power flow problem. The sine-cosine algorithm (SCA) is a modern method applied in numerical optimization problems. It consists of search randomly the best vector of control variables from the initial group of elements and oscillates to converge to the global optimum or diverge from it, functioning with a simple formulation based on sine and cosine mathematical functions with few setting parameters. In the proposed efficient sine cosine Algorithm (ESCA) the best values of setting parameters are chosen to give the best optimum solution with fast convergence. This technique improves the quality of the solution by exploring more search domain than the SCA method. The modified algorithm has been applied to the classical IEEE 30-Bus network with various objective functions and constraints. To make the comparison of ESCA and different recent algorithms, present results show the importance of ESCA to give the best and effective solution to the multi-objective optimal power flow problem.
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Optimal Reactive Power Dispatch Using Differential Evolution Algorithm with Voltage Profile Control
Статья научная
This paper proposes an efficient differential evolution (DE) algorithm for the solution of the optimal reactive power dispatch (ORPD) problem. The main objective of ORPD is to minimize the total active power loss with optimal setting of control variables. The continuous control variables are generator bus voltage magnitudes. The discrete control variables are transformer tap settings and reactive power of shunt compensators. In DE algorithm the other form of differential mutation operator is used. It consists to add the global best individual in the differential mutation operator to improve the solution. The DE algorithm solution has been tested on the standard IEEE 30-Bus test system to minimize the total active power loss without and with voltage profile improvement. The results have been compared to the other heuristic methods such as standard genetic algorithm and particle swarm optimization method. Finally, simulation results show that this method converges to better solutions.
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Статья научная
The essence of the technology business lies in the improvements and advancements that are continuously taking place in the industry. From vacuum tubes, diodes and transistors to the concepts of nano level designing have by and large created a revolution in the history of mankind. The biggest milestone in this journey has been the CMOS technology which has managed to survive for decades and is still an ongoing research area. However, advancing the technology includes many other dimensions which need to be taken care of. As the devices go on decreasing in size with the improving technology the power dissipation in them becomes a major issue. To counter this, a new logic called reversible logic has come into the pool of research. Further a shift from the transistor based paradigm is being explored to go down to ultra-small structures. A major breakthrough in this can be the Quantum Dot Cellular Automata (QCA) Nanotechnology. In this paper we have given a review about how the reversible logic and QCA nanotechnology together result in ultra-low power designs. Further we have optimized the design of Peres reversible gate using the concepts of explicit interaction of cells in QCA and verified the universal functionality using the optimized designs.
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Optimal Reliable Routing Path Selection in MANET through Novel Approach in GA
Статья научная
In MANETs (Mobile Adhoc Network) judgment in optimal reliable routing path between source and destination is a challenging task because of the mobility nature of nodes and is deficient in the infrastructure of the network which is so dynamic. So the objective of this paper is to identify an optimal reliable ordered routing paths between source and destination nodes in MANET.To meet the above challenging task the paper focus on an new novel approach in Genetic Algorithm called Parametric fitness based Genetic Algorithm.Proposed algorithm hybridized with classification model rough sets as one key sub component which offers better accuracy results.
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Статья научная
This paper presents binary differential evolution based optimal reporting cell planning (RCP) for location management in wireless cellular networks. The significance of mobile location management (MLM) in wireless communication has evolved drastically due to tremendous rise in the number of mobile users with the constraint of limited bandwidth. The total location management cost involves signaling cost due to location registration and location search and a trade-off between these two gives optimal location management cost. The proposed binary differential evolution (BDE) algorithm is used to determine the optimal reporting cell planning configuration such that the overall mobility management cost is minimized. Evidently, from the simulation result the proposed technique works well for the reference networks in terms of optimal cost and convergence speed. Further the applicability of the BDE is also validated for the realistic network of BSNL (Bharat Sanchar Nigam Limited), Odisha.
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Optimal machine learning model for software defect prediction
Статья научная
Machine Learning is a division of Artificial Intelligence which builds a system that learns from the data. Machine learning has the capability of taking the raw data from the repository which can do the computation and can predict the software bug. It is always desirable to detect the software bug at the earliest so that time and cost can be reduced. Feature selection technique wrapper and filter method is used to find the most optimal software metrics. The main aim of the paper is to find the best model for the software bug prediction. In this paper machine learning techniques linear Regression, Random Forest, Neural Network, Support Vector Machine, Decision Tree, Decision Stump are used and comparative analysis has been done using performance parameters such as correlation, R-squared, mean square error, accuracy for software modules named as ant, ivy, tomcat, berek, camel, lucene, poi, synapse and velocity. Support vector machine outperform as compare to other machine learning model.
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Статья научная
Indian practical rural distribution systems are very long and spread over a wide range of area. The nodes far away from the distribution substation are suffering from low voltage. In India, total distribution system losses are around 20% to 25%. From the past few years, penetration of distributed generation (DG) in to the distribution network/system is increasing expeditiously. DG allocation with appropriate location and size can provide numerous benefits to the distribution companies as well as to the society. In this regard, a new technique called combined sensitivity index (CSI), to find the optimal DG unit location, based on voltage sensitivity and network load magnitude is proposed. To assess the effectiveness of the proposed technique, it is tested on Indian practical 52-bus rural distribution system. The results obtained with the proposed CSI technique is compared with the results obtained with the combined power loss sensitivity (CPLS) technique. Here, the optimal DG unit size is calculated using Bird Swarm Algorithm (BSA). The results show that the proposed CSI technique performs better in minimizing power losses and voltage profile augmentation when compared to existing CPLS technique.
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Optimization of Fault Learning in Medical Devices
Статья научная
A relatively effective training system and advancements in data science demonstrate their evolutionary algorithm power to discover defects and abnormalities in the specified learning process. This work employs a fast and precise fault modelling environment to enhance genetic input implantable devices defect diagnostics. We offer a genetic data technique that incorporates phylogenetic analysis operations and faulty efficiency analysis. This study contributes to fault training in three different ways: 1) it exposes communicative training categories of information formulating adhesion, 2) it introduces a hierarchical system dissemination processing principles to design the fault aggregative, and 3) it indicates forecasting the genetic data sector that corresponds to complicated fault training. The proposed algorithm analyses methods that combine automatically generated fault detection development with massive data testing by non-repetitive fault instances. Analyzing data from validation challenges, infrastructure blowouts, and failure uncertainty make our algorithm more productive in the health sector.
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Optimization of Microgrid Using Quantum Inspired Evolutionary Algorithm
Статья научная
This paper presents a generalized formulation for determining the optimal operating strategy and cost optimization scheme as well as reducing the emissions of a MicroGrid (MG). In this article a microgrid including a wind turbine, pv array and a CHP system consisting of fuel cells and a microturbine is studied and then the modeling of various DERs is conducted and the objective functions and constraints are developed. The model takes into consideration the operation and maintenance costs as well as the reduction in emissions of NOx, SO2, and CO2 In the end the Quantum-Inspired Evolutionary Algorithm is employed to solved the optimal model and an operation scheme is achieved while meeting various constraints on the basis of tariff details, equipment performance, weather conditions and forecasts, load details and forecasts and other necessary information and then the economic costs and environmental impacts are analyzed and a conclusion that the QEA can achieve high environmental benefits and spend as low operation cost as possible. according to power Output functions and cost function of the various units , can be achieve to minimize cost.
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Optimization of Parameters at SDN Technologie Networks
Статья научная
A concept software-defined network is considered. Architecture of software-defined network is analyzed which, differently from traditional, foresee the separation of C-plane from a plane communication of data. The method of multicriterion optimization of multilevel networks is examined with determination of resulting objective function, which allows to carry out the synthesis of control system software-defined network (SDN) in the conditions of unforeseen changes of structure of the system.
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Optimization of secondary surveillance radar data processing
Статья научная
Secondary surveillance radar (SSR) performs one of the main functions of information service for consumers of the airspace control system. To improve the quality the SSR information is processed using modern information technology. The use of a consistent procedure for processing surveillance system data, due to the functionally completed stages of processing, made it possible to formalize the data processing procedure. However, this significantly limited, and in some cases excluded, the opportunities for inter-stage optimization of data processing. The SSR data processing structure synthesis and analysis are considered in this paper making it possible to perform a joint optimization of signal processing and primary processing of data, as well as to improve the quality of data processing.
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Optimized Angular a Star Algorithm for Global Path Search Based on Neighbor Node Evaluation
Статья научная
Any electromechanical device can be termed as Robot, which imitates human actions and in some of the situation can be used as a replacement for human. These days Robots are the integral part of our life and can be applied in several applications and tasks by giving respective commands. The research in robotics domain is to make it as autonomous and as much independent as it can be. The problem that arises is of controlling a mobile robot with the energy constraint. A lot of energy is wasted, if it takes wrong trajectory motion, this motion depends upon the robot knowledge which indeed in not constant. The variation in the environment results in making difficult for the robot to take precise and accurate measurements to reach the destination without much of the energy loss. An autonomous robot is expected to take decision according to the situation. For this precise decisions of robot path planning there are algorithms like A*, Dijkstra, D* etc. In this paper we have done analysis on partially known environment situation. Optimal path is planned by new heuristic approach over the A star algorithm, robot moving at an appropriate angle cuts down the unnecessary cost of path planning. Experimental results show that the proposed algorithm is much effective for more than 8% than the conventional A* algorithm in the same map environment.
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