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This paper presents three novel methods for speaker identification of which two methods utilize both the continuous density hidden Markov model (HMM) and the generalized fuzzy model (GFM), which has the advantages of both Mamdani and Takagi-Sugeno models. In the first method, the HMM is utilized for the extraction of shape-based batch feature vector that is fitted with the GFM to identify the speaker. On the other hand, the second method makes use of the Gaussian mixture model (GMM) and the GFM for the identification of speakers. Finally, the third method has been inspired by the way humans cash in on the mutual acquaintances while identifying a speaker. To see the validity of the proposed models [HMM-GFM, GMM-GFM, and HMM-GFM (fusion)] in a real-life scenario, they are tested on VoxForge speech corpus and on the subset of the 2003 National Institute of Standards and Technology evaluation data set. These models are also evaluated on the corrupted VoxForge speech corpus by mixing with different types of noisy signals at different values of signal-to-noise ratios, and their performance is found superior to that of the well-known models.

Opportunistic IoT networks operate in an intermittent, mobile communication topology, employing peer-to-peer transmission hops on a store-carry-forward basis. Such a network suffers from intermittent connectivity, lack of end-to-end route definition, resource constraints and uncertainties arising from a dynamic topology, given the mobility of participating nodes. Machine learning is an instrumental tool for learning and many histories-based machine learning paradigms like MLPROPH, KNNR and GMMR have been proposed for digital transformations in the field with varying degrees of success. This paper explores the dynamic topology with a plethora of characteristics guiding the node interactions, and consequently, the routing decisions. Further, the study ascertains the need for better representation of the versatility of node characteristics that guide their behavior. The proposed scheme Opportunistic Fuzzy Clustering Routing (OFCR) protocol employs a three-tiered intelligent fuzzy clustering-based paradigm that allows representation of multiple properties of a single entity and the degree of association of the entity with each property group that it is represented by. Such quantification of the extent of association allows OFCR a proper representation of multiple node characteristics, allowing a better judgement for message routing decisions based on these characteristics. OFCR performed 33.77%, 6.07%, 3.69%, 6.88% and 78.14% better than KNNR, GMMR, CAML, MLPRoPH and HBPR respectively across Message Delivery probability. OFCR, not only shows improved performance from the compared protocols but also shows relatively more consistency across the change in simulation time, message TTL and message generation interval across performance metrics. [ABSTRACT FROM AUTHOR]

This paper bestows 3-phase grid interfaced solar-wind hybrid renewable energy system (RES), feeding three-phase loads. The proposed system includes solar photovoltaic, permanent magnet based synchronous generator (PMSG), DC-DC converter, maximum power point tracker (MPPT) based on incremental conductance, three phases IGBT based voltage source converter (VSC), with a third order generalized integrator (TOGI) control technique. This control technique bestows multifunctional capabilities as harmonic mitigations, load balancing, and reactive power compensation. A fundamental component of load current is extracted by TOGI based controller, and further it is utilized to provide switching pulses to VSC for power quality enrichment. The fuzzy logic-based controller is used for loss computation of VSC as well as for maintaining DC link voltage. Moreover, fuzzy logic provides better dynamic performance compared to conventional PI controller. The results are presented in many aspects for linear and nonlinear loads such as, intermittent nature of solar and wind as well as disturbances in the system. A comparative analysis between proposed TOGI based controller and conventional control algorithm has been presented. Test results are performed by using MATLAB/ Simulink environment and demonstrate, AC-grid current is maintained within the IEEE-519 standard. [ABSTRACT FROM AUTHOR]

In this paper, the authors have addressed the modeling and design of the BLDC Motor-Driven E-Rickshaw based on hybrid energy storage system (HESS) for optimum power management using fuzzy logic. In Hybrid energy sources, solar power is used to charge a battery (primary source) that is effectively coupled to supercapacitor (ancillary source) for peak demand supplies. A power-split control strategy is proposed to control the power supply by using the HESS Fuzzy Logic in different engine operating modes. Projected power layering improves the battery life cycle with the proper use of the Supercapacitor. By providing a new switching algorithm, the DC link voltage is boosted to effectively transfer power to the HESS unit. Fuzzy logic-based HESS provides better performance in electric vehicles, such as deep discharge protection of the battery, and faster acceleration. Also, there is a quick comparison of E-rickshaw solar power with traditional E-rickshaw. The planned design model was simulated by MATLAB®/Simulink environment. [ABSTRACT FROM AUTHOR]

In this paper, a solar photovoltaic model integrated with brushless DC motor via DC to DC zeta converter is controlled in two stage. In first stage, a fuzzy rule based maximum power point tracking (PPT) is proposed to generate the pulse for DC to DC zeta converter. It is efficient intelligent control approach to extract maximum power from the solar PV system and enhance the speed to track the maximum power. The basic three process of fuzzy logic controller (FLC) are fuzzifier, inference and defuzzifier where the defuzzification process is used center of gravity (COG) method to convert its original value. The FLC to extract maximum PPT for solar PV based brushless DC motor can be examined the performance under transient and dynamic condition with different solar insolation. Moreover, in second stage a trapezoidal control approach based electronic commutation is chosen to generate the pulses of voltage source inverter (VSI) and it offers the smooth control of the brushless DC motor which can easily applicable for water pumping or irrigation purpose. A second stage, trapezoidal control approach is close loop control algorithm using sensorless drive. The performance of proposed fuzzy rule based control algorithm is shown using simulation results on MATLAB platform. [ABSTRACT FROM AUTHOR]

In this work, different types of power system faults at various distances have been identified using a novel approach based on Discrete S-Transform clubbed with a Fuzzy decision box. The area under the maximum values of the dilated Gaussian windows in the time-frequency domain has been used as the critical input values to the fuzzy machine. In this work, IEEE-9 and IEEE-14 bus systems have been considered as the test systems for validating the proposed methodology for identification and localization of Power System Faults. The proposed algorithm can identify different power system faults like Asymmetrical Phase Faults, Asymmetrical Ground Faults, and Symmetrical Phase faults, occurring at 20% to 80% of the transmission line. The study reveals that the variation in distance and type of fault creates a change in time-frequency magnitude in a unique pattern. The method can identify and locate the faulted bus with high accuracy in comparison to SVM. [ABSTRACT FROM AUTHOR]

The present work represents the implementation of the various fuzzy controller with robust sliding mode control (SMC) technique on a nonlinear system considering various external disturbances and model uncertainties. The nonlinear system considered here is a single link inverted pendulum. The proposed work combines the advantages of the sliding mode controlling technique and fuzzy logic controller. A set of linguistic rules are designed in fuzzy logic control, which causes the system to be chattering free. Parameters of the nonlinear system are adjusted according to fuzzy adaptive laws, while the uncertainties of the nonlinear system have been approximated using a fuzzy system. Various types of controller based on fuzzy sliding mode, like approximation based sliding mode control technique; equivalent control based fuzzy sliding mode technique, and switch-gain regulation based sliding mode control methods have been implemented here. A comparative analysis of various methods is also have been discussed. [ABSTRACT FROM AUTHOR]

The paper presents the application of artificial intelligence techniques-artificial neural network (ANN) and fuzzy logic to the problems of stock market prediction, sales forecasting and market segmentation. Back propagation algorithm has been used to train the neural network for the first two problems, while Self-Organizing Maps (SOM) have been employed for the clustering problem that Market Segmentation presents. The Fuzzy model discussed in the paper uses the state values or the input-output data pairs. Mean Square Error has been used as the performance index to compare ANN and Fuzzy logic for stock prediction and sales forecasting. [ABSTRACT FROM AUTHOR]

Quality management is for providing better resources to the customers. It is applicable for hospitals for giving best services. QM has an initiative i.e. FMEA, which is applicable in the purchasing process of the hospital (PPH) for calculating the RPN, which determines the risks linked with the problems occurring in purchasing the particular equipment for the hospital. RPN is conquered from past experience and engineering decisions which leads to errors and discrepancies. In this study, Neuro-Fuzzy approach based technique is applied to improve the purchasing process in Indian private hospitals. Neuro-Fuzzy approach eliminates insufficiencies in the assessment of the RPN, resulting in saving of time. PPH in India has never been improved before by applying neuro-fuzzy scheme based FMEA technique. Analyzed results show that the applied neuro-fuzzy method is able to solve the problems which arise from conventional FMEA approach and will effectively find out RPN. Proposed method provides quality assurance in the process. [ABSTRACT FROM AUTHOR]

In cloud computing datacenter infrastructure, virtualization has enabled service providers to create abstraction of their physical resources and increased the infrastructure utilization. At the datacenter, cloud users services are implemented using Virtual Machines (VMs). With the popularity of cloud paradigm, numbers of enterprise applications deployed over cloud systems are increasing. This shift in cloud paradigm has brought new challenges of server consolidation, load balancing, resource management, and server fault situation. Cloud managers trigger VM migration to achieve all these challenges. VM migration allows migration of running VMs from one datacenter to another. In migration process to keep the VM alive, VM memory pages are transferred in multiple iterations. Thus, there is an increase in the requirement of network resources consumption during migration process. However, inaccurate bandwidth allocation for a VM migration request can lead to performance degradation. Therefore, there is a need to devise an efficient bandwidth allocation strategy. In this paper, we propose a multistage bandwidth allocation technique which aims to minimize the bandwidth allocation at each round of migration mechanism. The proposed technique is implemented by the simulation carried out in Matlab software. To ensure the applicability for real-time hosted applications, multistage scheme is evaluated using both low and high dirty rate conditions. We also considered distributed cloud deployment situation by using sequential and multiple VM migration test cases. The results demonstrated shows, as compared to existing VM migration bandwidth allocation schemes, our proposed technique outperform in terms of downtime, migration time, bandwidth provisioned and migration iterations count. Additionally, we also explored multistage technique with fuzzy logic to improve the VM selection process. The significance of this study is that, multistage technique allows bandwidth allocation in each iteration as variable to current dirty rate scenario, thereby increasing the revenue of cloud providers. [ABSTRACT FROM AUTHOR]

A two link planar rigid robotic manipulator is a highly nonlinear, coupled and multi-input multi-output system. For its effective control, an intelligent adaptive fractional order fuzzy sliding mode proportional integral and derivative controller (FOFSMCPID) has been presented in this work. Sliding mode controller (SMC) is designed by using exponential law and closed loop stability analysis is demonstrated by using Lyapunov theorem. The chattering in the SMC controller has been effectively reduced with the help of boundary layer along with the fuzzy logic. For tuning of the controller, a weighted sum of integral of absolute error and chatter has been considered as the objective function to be minimized using cuckoo search optimization algorithm. To demonstrate the efficacy of FOFSMCPID controller, the results have been compared with integer order fuzzy sliding mode proportional, integral and derivative controller (IOFSMCPID), integer order fuzzy sliding mode proportional and derivative controller (IOFSMCPD) and fractional order fuzzy sliding mode proportional and derivative (FOFSMCPD) controller for trajectory tracking, disturbance rejection, noise suppression and model uncertainties. The detailed presented investigations have demonstrated that FOFSMCPID controller exhibits much superior performance over IOFSMCPID, IOFSMCPD and FOFSMCPD controllers. [ABSTRACT FROM AUTHOR]

Speed control of synchronous machines using Field Oriented Control (FOC) classically uses Proportional Integral (PI) or Integral Proportional (IP) regulators that allow to achieve satisfactory goals on the dynamics of speed and torque. However, the performance deteriorates with loss of one or more phases in multiphase machines with IP regulator. This paper present comparison between the use of IP regulator and Fuzzy Logic Regulator (FLR) under same conditions applied to Five Phase Permanent Magnet Synchronous Machine (FPSM). First, modeling and performance of the FPSM are presented. In the beginning, the control is ensured in healthy mode with an IP regulator then in degraded mode when one then two phases are opened. The performances of the FLR are compared to IP ones. Better performance of FLR is established in terms of faster dynamics. [ABSTRACT FROM AUTHOR]

This paper deals with MATLAB/SIMULINK simulation and analysis of a position sensor-less field oriented control of permanent magnet synchronous motor. Adaptive position estimators are required as the parameters of the machines like rotor resistance, inductance changes sometimes. Adaptive position and speed estimators viz. SMO, MRAS are much discussed in literature but the artificial neural network, adaptive neuro-fuzzy inference based estimators are least discussed. In this paper a MATLAB study of MRAS, ANN and ANFIS based position estimator in a Field oriented control of a permanent magnet synchronous motor drive is being done. MRAS, ANN, ANFIS estimators adaptive in nature so these estimators can adapt if there is any parameters change online. The performances of these three drives are analyzed, and results are compared. It is seen that ANFIS based system performance is better even when the parameters of the machines vary with time. This work is limited to analysis and simulation only and could be extended to a practical realization in future work. [ABSTRACT FROM AUTHOR]

DC power supply is required by majority of power electronics devices such as Uninterrupted Power Supply, rectifier Switch Mode Power Supply etc. These draw highly distorted input current of nonlinear nature for a short time. This results in higher total harmonic distortion (THD) due to interference in other electrical equipments. The problems related to poor power factor are mitigated by Power factor correction (PFC) techniques. A comparative study based on type of frequency, current amplifier IC's used etc. This paper explains the development of fuzzy logic-based PFC operating in continuous conduction mode. Analysis has been carried out for different load and power factor is seen to improve with increase in the load. [ABSTRACT FROM AUTHOR]

The battery era has started to compensate the demand of the energy while the charging issues still exist. Thus, demand of reliable and optimized charging is required to charge cell/battery. In this paper novel optimized technique is proposed, based on gravitational search algorithm (GSA) to charge e-rickshaw battery using single sensor based maximum power point tracking (MPPT) of solar photovoltaic (SPV) module. There are various metaheauristic and heuristic techniques are available like Cauchy and Gaussian sine cosine optimization (CGSCO) intelligent technique, evolutionary algorithms, stochastic algorithms, Swarm optimization technique, ant colony technique, neural algorithms, fuzzy logic algorithms to optimize the charging current of cell/battery. These techniques take more iteration to give the optimal solution. Moreover, GSA is the high level intelligent technique which is used in multi area to optimize the various parameters in engineering fields. It is very ease to find the optimal solution in search space. This approach is novel in the field of e-rickshaw battery charging. Therefore, the mathematical algorithm based on GSA has been developed to optimize the current of charging cell/battery. The performance of GSA optimization technique is verified and compared with the metaheauristic based CGSCO optimization technique. It is observed that GSA is easy to design and reduce the cost of charger. [ABSTRACT FROM AUTHOR]

A unified power quality conditioner (UPQC) has two converters interlinked with back to back DC voltage source. The UPQC is used to improve the multiple power quality (MPQ) of the AC distribution network. The quality of the UPQC depends on the selection of series and shunt control algorithms. The series converter injects the voltage and improves voltage quality whereas the shunt converter injects current and improves the current quality of the AC distribution network. The UPQC works properly if the DC link voltage maintains constant under abnormal or normal condition of AC grid system. Thus, in this paper, DC link voltage is controlled using intelligent model, adaptive neuro fuzzy inference system (ANFIS). The ANFIS result is compared with fuzzy model and conventional PI controller to enhance the mitigation capability of the UPQC. Moreover, the series and shunt converters are controlled by modified second order generalised integrator (SOGI) based algorithm. The performance of intelligent control model is verified by MATLAB based simulation results. [ABSTRACT FROM AUTHOR]

We propose a fuzzy Reinforcement learning (FRL) framework for an efficient solution to the Economic thermal power dispatch (ETPD) considering multiple fuel options along with valve point loading effect concerning with thermal power generating units. The objective of ETPD is optimizing operating cost for specified power demand meet and to satisfy the generation capacity limits of each unit. In the presented work, We cast the ETPD as a multi agent FRL (MAFRL) problem wherein individual thermal generators act as players for minimizing operational cost and also satisfying the generation limits of each units to obtain a specified power demand. To prove supremacy and validity of proposed multi agent fuzzy reinforcement learning technique, two benchmark test systems involving 10 and 40 units integrated using numerous fuel systems with valve point loading effect have been simulated. Simulation results and comparison against several other existing solution approaches showcases the efficacy of MAFRL technique in solving the ETPD problem. [ABSTRACT FROM AUTHOR]

This paper presents a novel scheme called “Laboratory Testbed for Embedded Fuzzy Control of a Real Time Nonlinear System.” The idea is based upon the fact that project-based learning motivates students to learn actively and to use their engineering skills acquired in their previous years of study. It also fosters initiative and focuses students' attention on authentic real-world problems. At the same time, it enhances their learning. Students gain hands-on experience and improve their skills in product development, self-directed learning, teamwork, and project management. There has been a tremendous rise in the popularity of intelligent control techniques like fuzzy logic for use in industrial control applications in recent times. These techniques, which were primarily conceived for nonlinear control applications, are best implemented using embedded controllers, which can use their capabilities to the maximum. While courses in electrical and computer engineering cover several areas like digital and analog integrated circuits, microprocessors and control Systems, and process control, few of these integrate all these areas with focus on the application of intelligent techniques in real-time systems. Also, there is a growing need in industry for engineers who can perform software design and system integration for various applications in embedded control. This paper aims at developing such a practical task as one of the major projects in the eighth semester of the program offered by the Instrumentation and Control Engineering (ICE) Department of Netaji Subhas Institute of Technology (NSIT), Delhi University, India, to design a real-time embedded controller implementing an intelligent control technique, fuzzy logic, for control applications. These applications might, for example, be level control, flow control, or pressure control. The paper discusses an example of a real-time pressure control system for which a microcontroller-based fuzzy proportional-integral-derivative (PID) controller has been simulated and implemented with satisfactory results. [ABSTRACT FROM AUTHOR]