• Big data analytics architecture design-taliem-ir

    Big data analytics architecture design—an application in manufacturing systems


    Context: The rapid prevalence and potential impact of big data analytics platforms have sparked an interest amongst different practitioners and academia. Manufacturing organisations are particularly well suited to benefit from data analytics platforms in their entire product lifecycle management for intelligent information processing, performing manufacturing activities, and creating value chains. This requires re-architecting their manufacturing  legacy information systems to get integrated with contemporary data analytics platforms. A systematic re-architecting approach is required incorporating careful and thorough evaluation of goals for data analytics  doption. Furthermore, ameliorating the uncertainty of the impact the new big data architecture on system quality goals is needed to avoid cost blowout in implementation and testing phases. Objective: We propose an approach to reason  about goals, obstacles, and to select suitable big data solution architecture that satisfy quality goal preferences and constraints of stakeholders at the presence of the decision outcome uncertainty. The approach  will highlight situations that may impede the goals. They will be assessed and resolved to generate complete requirements of an architectural solution. Method: The approach employs goal-oriented modelling to identify obstacles causing quality goal failure and their corresponding resolution tactics. It combines fuzzy logic to explore uncertainties in solution architectures and to find an optimal set of architectural decisions for the big data enablement process of manufacturing systems. Result: The approach brings two innovations to the state of the art  of big data analytics platform adoption in manufacturing systems: (i) A systematic goal-oriented modelling for exploring goals and obstacles in integrating manufacturing systems with data analytics platforms at the requirement level and (ii) A systematic analysis of the architectural decisions under uncertainty incorporating stakeholders’ preferences. The efficacy of the approach is illustrated with a scenario of reengineering a hyper-connected manufacturing collaboration system to a new big data architecture.


  • A fuzzy logic based multi-agents controller[taliem.ir]

    A fuzzy logic based multi-agents controller


    This paper presents a fuzzy logic based controller (Multi-Agents System Controller (MASC)) which regulates the number of agents released to the network on a Multi-Agents Systems (MASs). A fuzzy logic (FL) model for the controller is as presented. The controller is a two-inputs-one- output system. The controllability is based on the network size (NTZ) and the available bandwidth (ABD) which are the inputs to the controller, the controller’s output is number of agents (ANG). The model was simulated using SIMULINK software. The simulation result is presented and it shows that ABD is the major constraint for the number of agents released to the network.

  • A Fuzzy Multiple Criteria Decision Making Model in Employee.[taliem.ir]

    A Fuzzy Multiple Criteria Decision Making Model in Employee Recruitment


    This study is intended to improve the lack of recruitment processes as well as reduce individual senses of supervisory level by fuzzy logic and Analytic Hierarchy Process methods. This study tries to identify  appropriate personality traits and key professional skills through the information statistics and analysis of Analytic Hierarchy Process in order to expect the recruitment process be more reasonable based on the fuzzy multiple criteria decision making model to achieve the goal of merit-based selection. The results showed that the fuzzy multiple criteria model constructed in this study could indeed solve the shortcomings in existing enterprises’ recruitment, and provide more information for decision-making reference.

  • Application of Fuzzy Logic in Determining Cost of Capital for.[taliem.ir]

    Application of Fuzzy Logic in Determining Cost of Capital for the Capital Budgeting Process


    The capital budgeting process is based on the technique of reducing future cash flows of the net present  value which implies a process of discounting by using the discount rate. Usually, in capital budgeting process the discount rate is presented through the cost of capital. The determination of the cost of capital primarily depends on the capital structure, but since the process of capital budgeting implies a long time period, it also implies uncertainty and vagueness. Subjective perception, thinking, judgment and decision making, including a large number of predicted vague data is often expressed solely in linguistic variables by the management and this is the main characteristic of the capital budgeting process, especially in the determination of the cost of the capital through a long time period. The main intention of this paper is to present the use of fuzzy logic in the process of determining the cost of capital and providing an alternative approach in the appraisal of the cost of capital in the presence of fuzziness. The integration and implementation of linguistic variables i.e.  qualitative information in the determination of the cost of capital using fuzzy numbers in the capital budgeting process will also be discussed. Through the formulation of a fuzzy system and the use of fuzzy numbers we will propose a process and methodology* for the use of fuzzy numbers in the process of the cost of capital determination. We examine the presented methods and suggest new ideas that could improve further  research and implementation of fuzzy logic in the capital budgeting process.

  • Cluster Head Selection using a Two-Level Fuzzy Logic in Wireless Sensor Networks[taliem.ir]

    Cluster Head Selection using a Two-Level Fuzzy Logic in Wireless Sensor Networks


    Due to resource limitations in wireless sensor networks, prolonging the network lifetime has been of a great interest. An efficient routing technique is known as hierarchical routing based on clustering, in which finding the optimum cluster heads and number of them has been a challenge. In this paper, a two-level fuzzy logic is utilized to evaluate the qualification of sensors to become a cluster head. In the first level (Local Level), the qualified nodes are selected based on their energy and number of neighbors of them. Then, in the second level (Global Level), nodes’ overall cooperation is considered in the whole network with three fuzzy parameters. These parameters are centrality, proximity to base station and distance between cluster heads. Simulation results in five metrics show that the proposed approach consumes less energy and prolongs the network life time about 54 % compared with other algorithms.

  • Cluster-head Election using Fuzzy Logic for Wireless Sensor Networks[taliem.ir]

    Cluster-head Election using Fuzzy Logic for Wireless Sensor Networks


    Wireless Sensor Networks (WSNs) present a new generation of real-time embedded systems with limited computation, energy and memory resources that are being used in a wide variety of applications where traditional networking infrastructure is practically infeasible. Appropriate cluster-head node election can drastically reduce the energy consumption and enhance the lifetime of the network. In this paper, a fuzzy logic approach to cluster-head election is proposed based on three descriptors – energy, concentration and centrality. Simulation shows that depending upon network configuration, a substantial increase in network lifetime can be accomplished as compared to probabilistically selecting the nodes as cluster-heads using only local information.

  • Fuzzy Logic Based Method of Speed Control of DC Motor[taliem.ir]

    Fuzzy Logic Based Method of Speed Control of DC Motor


    Various method of speed control of DC motor is vailable in the literature. This paper presents design and implements of fuzzy logic in the speed control of DC motor. Fuzzy logic has found high application as a speed control techniques because of its ability to take into account vague and uncertainties . This paper presents a MATLAB simulink model for speed control of DC motor using fuzzy logic.

  • Intelligent Frequency Control in an AC Microgrid[taliem.ir]

    Intelligent Frequency Control in an AC Microgrid: Online PSO-Based Fuzzy Tuning Approach


    Modern power systems require increased intelligence and flexibility in the control and optimization to ensure the capability of maintaining a generation-load balance, following serious disturbances. This issue is becoming more significant today due to the increasing number of microgrids (MGs). The MGs mostly use renewable  energies in electrical power production that are varying naturally. These changes and usual uncertainties in power systems cause the classic controllers to be unable to provide a proper performance over a wide range of operating conditions. In response to this challenge, the present paper addresses a new online intelligent approach by using a combination of the fuzzy logic and the particle swarm optimization (PSO) techniques for optimal tuning of the most popular existing proportional-integral (PI) based frequency controllers in the ac MG systems. The control design methodology is examined on an ac MG case study. The performance of the proposed intelligent control synthesis is compared with the pure fuzzy PI and the Ziegler-Nichols PI control design methods .

  • Non-Intrusive Fall Detection-taliem-ir

    Non-Intrusive Fall Detection Monitoring for the Elderly Based on Fuzzy Logic


    This paper presents a health condition monitoring solution that detects an elderly accidental fall occurrence. The fall detection algorithm implements both accelerometer-based and sound-based detections for the possible occurrence of a valid fall. The accelerometer-based fall detection is instrumental in the detection of a valid fall occurrence. However, it has been shown that by using accelerometer alone is insufficient to accurately detect a fall, as the accelerometer also misinterprets some daily motion activities and classified them as valid falls. The sound sensor can be used to detect the sound pressure generated from a resultant fall, but sound pressure cannot by itself be used as a reliable indicator of a fall. Thus, a fuzzy logic-based fall detection algorithm is developed to process the output signals from the accelerometer and sound sensor, where a valid fall activity detected by the accelerometer, coupled with a detected sound pressure from the resultant fall can infer an occurrence of a valid fall. This paper demonstrates the fuzzy logic algorithm to improve the accuracy of detecting a valid fall as compared to the accelerometer only fall detection algorithm and it can be demonstrated that the algorithm is capable of minimizing false fall detections per day from high of 1:37 to low of 0:06.

  • Power System Stabilizer Design Using Local and[taliem.ir]

    Power System Stabilizer Design Using Local and Global signals


    In this paper the feasibility of fuzzy logic based power system stabilizer with local and remote inputs is  presented. Using global signals with the aid of Global Positioning System (GPS) and Wide Area Measurement (WAM) increases the possibility of global vision of power system and better damping to interarea oscillations. We have taken two input fuzzy logic controller for our study. The local input signal, generator rotor speed deviation is used to damp the local mode oscillations. The global signal obtained from WAM, such as Area differential frequency or Tie line active power deviation is used to damp the interarea oscillations. In the study, both transient and small signal stability analysis are used to determine the performance of study system.

  • Reactive power control for improving voltage profiles A comparison between[taliem.ir]

    Reactive power control for improving voltage profiles: A comparison between two decentralized approaches


    This paper is concerned with a local regulation of the voltage profiles at buses where wind power distributed generators are connected. In particular, the aim of the work is to compare two voltage control methods: the first based on a sensitivity analysis and the second on the designing of a fuzzy control system. The two  methods are tested by means of simulations on a real distribution system and the results indicate that both methods allow the voltage profiles to be regulated at the wind generator connection bus within voltage standard limits, by taking into account the capability curves of the wind generators. Nevertheless, the fuzzy method presents more advantage in comparison with the sensitivity method. In fact, (i) it provides a gentler action control with a lower reactive power consumption during control operations as the reactive power profile follows better the voltage variations; (ii) the design of the fuzzy controller is independent from the knowledge of network parameters and its topology.

  • Using fuzzy Ant Colony-online-taliem.ir

    Using fuzzy Ant Colony Optimization for Diagnosis of Diabetes Disease


    Ant colony optimization (ACO) has been used successfully in data mining field to extract rule based classification systems. The Objective of this paper is to utilize ACO to extract a set of rules for diagnosis of diabetes disease. Since the new presented algorithm uses ACO to extract fuzzy If-Then rules for diagnosis of diabetes disease, we call it FADD. We have evaluated our new classification system via Pima Indian Diabetes data set. Results show FADD can detect the diabetes disease with an acceptable accuracy and competitive or even better than the results achieved by previous works. In addition, the discovered rules have good comprehensibility.