Journal of Intelligent & Fuzzy Systems - Volume 36, issue 6

Authors: Nazari, M.H. | Hosseinian, S.H. | Azad-farsani, E.

Article Type: Research Article

Abstract:  This paper presents a new electricity pricing methodology in distribution networks by imploying Distributed Generations (DGs). As long as the Locational Marginal Price (LMP) is used in the pricing of short-term operations as an efficient method, it can be performed in distribution network consequently. The proposed pricing method is modeled as an optimization problem with the specific control variables and objectives. The variables are LMPs and DGs power factors, and objectives are total losses and emission. Also, profit earned from reduction of loss and emission was allocated between DGs. Reduction of loss and emission was compensated by DGs production. As …a result, more production resulted to high price of DG buses rather than market price. This price should be provided by Distribution Company (DISCO), and DISCO earns this money form the profit. Because of the multi-objective nature of the problem, a Multi-Objective Genetic Algorithm Optimization (MOGA) is implemented to solve. In order to validate the proposed method, a comparison between MOGA and Multi-Objective Particle Swarm Optimization (MOPSO) is performed consequently. The proposed method allows the decision-makers to apply their preferences among losses/emission reduction and DISCO’s benefit. furthermore, the feasibility of the proposed method is investigated using the IEEE-32 bus test system. Show more

Keywords: Electricity price, multi-objective optimization, locational marginal price, loss reduction, emission reduction

DOI: 10.3233/JIFS-181990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6143-6154, 2019

Authors: Araghi, Sahar | Khosravi, Abbas | Creighton, Douglas

Article Type: Research Article

Abstract: This paper compares the performance of three different types of traffic signal timing controllers that all of them apply fuzzy logic systems in their controlling structure. The comparisons are done between an interval type-2 adaptive neuro-fuzzy inference system (IT2-ANFIS) traffic signal controller, a type-1 ANFIS (T1-ANFIS), and a fixed-fuzzy logic, and three different fixed-time controllers as benchmarks. Obtained results demonstrate the superiority of the fuzzy controller over traditional controllers. The traffic network used in experiments is a network of nine intersections and all controllers are designed in distributed form. Each intersection has its own controller while it considers its neighbor …intersections’ traffic jam in determining signal time for each traffic phase. The average performance of IT2-ANFIS, T1-ANFIS, and fixed-fuzzy controllers against the fixed-time controller are approximately 35%, 30%, and 8%, respectively. Show more

Keywords: Traffic Signal Timing, Type-2 fuzzy logic systems, Fuzzy Logic Systems, ANFIS

DOI: 10.3233/JIFS-181993

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6155-6166, 2019

Authors: Liu, Zhimin | Qu, Shaojian | Goh, Mark | Huang, Ripeng | Wang, Shilei

Article Type: Research Article

Abstract: This paper applies the credibility distribution of fuzzy variables in uncertainty theory to formulate a fuzzy optimization model, so as to study the coordination mechanism of a distribution supply chain system with fuzzy demand. This practical system, focusing on the demand side of the supply chain, comprises multi-period, multi-manufacturer, multi-retailer, and multi-consumer. Next, using a novel modified sequence quadratic programming algorithm, the optimal order quantity, sales volume of the retailers, and the maximum expected return of the distribution supply chain are obtained. The effects of the wholesale price, retail price, and inventory cost on the expected return are also analyzed. …Finally, the usfulness of the proposed modified sequence quadratic programming algorithm is compared against the conventional MATLAB optimal toolbox and genetic algorithm. Our computational results validate in favour of the proposed algorithm in terms of the computational time, number of iterations, and convergence rate. Show more

Keywords: Distribution supply chain, fuzzy demand, uncertainty theory, algorithm

DOI: 10.3233/JIFS-181997

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6167-6180, 2019

Authors: Jahan, Hosney | Feng, Ziliang | Mahmud, S.M. Hasan | Dong, Penglin

Article Type: Research Article

Abstract: Regression testing involves validating a software system after modification to ensure that the previous bugs have been fixed and no new error has been raised. Finding faults early and increasing the fault detection rate are the main objectives of regression testing. A common technique involves re-executing the whole test suite, which is time consuming. Test case prioritization aims to schedule the test cases in an order that could achieve the regression testing goals early in the testing phase. Recently, machine learning techniques have been extensively used in regression testing to make it more effective and efficient. In this paper, we …propose and investigate whether an Artificial Neural Network (ANN)-based approach can improve the version specific test case prioritization approach. The proposed approach utilizes the combination of test cases complexity information and software modification information with an ANN, for early detection of critical faults. Three new factors have been proposed, based on which an ANN is trained and finally it can automatically assign priorities to new test cases. The proposed approach is empirically evaluated with two software applications. Effectiveness metrics, such as fault detection rate, accuracy, precision, and recall are examined. The results suggest that the proposed approach is both effective and feasible. Show more

Keywords: Regression testing, test case prioritization, artificial neural network, fault detection capability

DOI: 10.3233/JIFS-181998

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6181-6194, 2019

Authors: Vamshi Krishna, S. | Srivastava, Azad | Wagh, Sunil J. | Sabbi, Santhi

Article Type: Research Article

Abstract:  The traditional Job Management System (JMS) handles only physical resources as computational resources. Computational resources are assigned to a job as computing nodes in HPC (High Performance Computing) cluster system and the job processes are executed directly on allocated computing nodes. Thus the prevailing method of HPC network does not considers the process of Self-healing. Self-healing mechanism includes features like self-detection, self-repairing and self-configuring in the infrastructure of network towards maximising the reliability, resilience, safety and availability of the HPC network. Thus in our framework by considering a self-healing Software Defined Networking (SDN)-aware SH work, which formally analyse the trade-offs …between timeliness and volume of the load information being revealed which enables the efficiency and granularity of the control input necessary to achieve fast reconfiguration which in turn enabling the throughput of the HPC network to be improved. The framework comprises the SDN accelerated HPC network for resource allocation using mongrel of FA-CRA (Fairness Aware Cooperative Resource Allocation) and CG-CRA (Coalition Game Based Resource Allocation) Algorithms and in order to make the resource allocation more effective manner an absolute Job scheduling is done by combining the priority based scheduler DLS (Dynamic Level Scheduling) and time based scheduler ETF (Earlier Time First). Thus the throughput of the system is improved. This framework can be implemented in NS2 Simulation. Show more

Keywords: FA-CRA resource allocation, CG-CRA resource allocation, HPC network, JMS network, D L Scheduling, ETF Scheduling

DOI: 10.3233/JIFS-182025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6195-6206, 2019

Authors: Zhao, Zhongying | Zhou, Hui | Zhang, Bijun | Ji, Fujiao | Li, Chao

Article Type: Research Article

Abstract: High influential users are playing an important role in promoting information propagation in social media. Thus, it has been a very interesting problem to identify influential users in social media, and attracted numerous researchers. A great deal of research work has been devoted to solving this problem. However, the existing methods mainly focus on the network topology, ignoring users’ behaviors. In this paper, we propose an High Influential Users Detection (HIUD) algorithm by analyzing users’ behaviors. To evaluate the performance of our algorithm, we carry out extensive experiments on Sina and Tencent microblogging data sets, and compare it with other …methods. The experimental results have shown that the HIUD achieves the best performance. Furthermore, we also make a spatial analysis on those high influential users with thermodynamic map. Show more

Keywords: Influential user detection, user behavior analysis, clustering, social media data mining

DOI: 10.3233/JIFS-182512

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6207-6218, 2019

Authors: Fan, Zongwen | Chiong, Raymond | Hu, Zhongyi | Dhakal, Sandeep | Lin, Yuqing

Article Type: Research Article

Abstract: Residuary resistance prediction is an important initial step in the process of designing a sailing yacht. Being able to predict the residuary resistance accurately is crucial for calculating the required propulsive power and ensuring good performance of the sailing yacht. This paper presents a two-layer Wang-Mendel (WM) fuzzy approach to improve the approximation ability of the WM model for this prediction task. Unlike the traditional WM method, in which the consequent of its fuzzy rules is a fuzzy set, the consequent of our proposed approach corresponds to a fuzzy rule base. We apply a top-down method and fuzzy-rule clustering to …construct the two-layer WM model, while a bottom-up method is employed to predict the residuary resistance. Experimental results based on two benchmark functions and a yacht hydrodynamics application show that the proposed approach is able to obtain improved robustness and accuracy in predicting residuary resistance compared to other WM model variants and well-known machine learning algorithms. Show more

Keywords: Two-layer Wang-Mendel model, top-down construction, bottom-up prediction, prediction of residuaryresistance

DOI: 10.3233/JIFS-182518

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6219-6229, 2019

Authors: Kumar, Ravinder | Bansal, Hari Om | Agrawal, Hanuman Prasad

Article Type: Research Article

Abstract:  This paper presents the design, modeling, and implementation of a photovoltaic (PV) integrated shunt active power filter (SAPF) for clean power generation along with power quality improvement. The system uses fuzzy logic control (FLC) for maximum power point tracking (MPPT) and SAPF control. The proposed system implements synchronous reference frame theory based direct power control strategy using hysteresis control. The system performance is evaluated in real-time under-balanced/unbalanced and nonlinear load conditions using field-programmable gate array (FPGA) based computational engine with different controllers. The performance is observed to be better with FLC as compared to traditional controllers.

Keywords: Photovoltaic array, maximum power point, fuzzy logic control, shunt active power filter, power quality

DOI: 10.3233/JIFS-182520

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6231-6243, 2019

Authors: Wang, Lei | Li, Na

Article Type: Research Article

Abstract:  The interval-valued Pythagorean fuzzy set (IVPFS), as a new generalization of Pythagorean fuzzy set, which can be used to model complex uncertainty in the real multiple attribute group decision making (MAGDM) problems. This paper aims to develop some new operators for dealing with MAGDM issue with interval-valued Pythagorean fuzzy information, such as continuous interval-valued Pythagorean fuzzy ordered weighted quadratic averaging (C-IVPFOWQA) operator, weighted C-IVPFOWQA operator, ordered weighted C-IVPFOWQA operator and hybrid C-IVPFOWQA operator. The proposed operators not only improve the accuracy of decision making but also can reflect the decision makers’ risk preferences. Additionally, some desirable properties and special cases …of these operators are investigated in detail. Later, we present a MAGDM method based on the hybrid C-IVPFOWQA operator. Finally, a numerical example is presented to illustrate the proposed method and comparison analysis is given to demonstrate the practicality and effectiveness. Show more

Keywords: Continuous interval-valued Pythagorean fuzzy ordered weighted quadratic averaging (C-IVPFOWQA) operator, weighted C-IVPFOWQA operator, ordered weighted C-IVPFOWQA operator, hybrid C-IVPFOWQA operator, multiple attribute group decision making

DOI: 10.3233/JIFS-182570

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6245-6263, 2019

Authors: Khan, Asghar | Izhar, Muhammad | Hila, Kostaq

Article Type: Research Article

Abstract: In this paper, we apply the concept of double-framed soft set (briefly DFS -set) to non-associative and non-commutative structure called Abel Grassmann’s groupoid (briefly AG -groupoid). We define double-framed soft quasi-ideals (briefly DFS quasi-ideal) of AG -groupoids and discuss their properties in left regular AG-groupoids. We also characterize left regular AG -groupoids in terms of DFS quasi-ideals. Application of DFS sets in decision making situation is provided.

Keywords: DFS-set, DFS AG-groupoid, DFS int-uni product, DFS quasi-ideals, Left regular AG-groupoids, Decision making

DOI: 10.3233/JIFS-182572

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 6265-6281, 2019