International Journal of Computer and Applications

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

This paper proposes an energy management and control system for laboratory scale microgrid based on hybrid energy resources such as wind, solar, and battery. Power converters and control algorithms have been used along with dedicated energy resources for the efficient operation of the microgrid. The control algorithms are developed to provide power compatibility and energy management between different resources in the microgrid. It provides stable operation of the control in all microgrid subsystems under various power generation and load conditions. The proposed microgrid, based on hybrid energy resources, operates in autonomous mode and has an open architecture platform for testing multiple different control configurations. A real-time control system has been used to operate and validate the hybrid resources in the microgrid experimentally. The proposed laboratory scale microgrid can be used as a benchmark for future research in smart grid applications.

Energy Management and Control System for Laboratory Scale Microgrid Based Wind-PV-Battery

Global energy challenges have driven the adoption of renewable energy sources. Usually, an intelligent energy and battery management system is deployed to harness the renewable energy sources efficiently, whilst maintaining the reliability and robustness of the power system. In recent years, the battery-supercapacitor based hybrid energy storage system (HESS) has been proposed to mitigate the impact of dynamic power exchanges on battery's lifespan. This study reviews and discusses the technological advancements and developments of battery-supercapacitor based HESS in standalone micro-grid system. The system topology and the energy management and control strategies are compared. The study also discusses the technical complexity and economic sustainability of a standalone micro-grid system. A case study of a standalone photovoltaic-based micro-grid with HESS is presented.

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Battery-supercapacitor hybrid energy storage system in standalone DC microgrids: areview

Bridge risks are often evaluated periodically so that the bridges with high risks can be maintained timely. This paper develops an adaptive neuro-fuzzy system (ANFIS) using 506 bridge maintenance projects for bridge risk assessment, which can help Highways Agency to determine the maintenance priority ranking of bridge structures more systematically, more efficiently and more economically in comparison with the existing bridge risk assessment methodologies which require a large number of subjective judgments from bridge experts to build the complicated nonlinear relationships between bridge risk score and risk ratings. The ANFIS proves to be very effective in modelling bridge risks and performs better than artificial neural networks (ANN) and multiple regression analysis (MRA).

An adaptive neuro-fuzzy inference system for bridge risk assessment

https://cyberleninka.org/article/n/448947.pdf

Renewable energy management through microgrid central controller design: An approach to integrate solar, wind and biomass with battery

A neuro-fuzzy computing provides the system identification and interpretability of fuzzy models and learning capability of neural networks in a single system. In the last decade, various neuro-fuzzy systems have been developed. Among them, adaptive neuro-fuzzy inference system (ANFIS) provides a systematic and directed approach for model building and gives the best possible design parameters in minimum time. They have got wide acceptance for modelling many real world problems. One such problem frequently encountered is the effects of noise pollution on human work efficiency. From the literature survey, it is observed that the three most important factors influencing human work efficiency are noise level, type of task, and exposure time. The cause effect relationships of these parameters are complex, uncertain, and non-linear in nature. Therefore, it is quite difficult to properly examine it by conventional methods. Hence, an attempt is made in this paper to develop a neuro-fuzzy model for predicting the effects of noise pollution on human work efficiency as a function of noise level, type of task, and exposure time. The model is implemented on Fuzzy Logic Toolbox of MATLAB^(C) using ANFIS. The data used in the present study is obtained with the help of the original fuzzy model developed by the authors. Out of the total input/output data sets, 80% was used for training the model and 20% for checking to validate the model.

A neuro-fuzzy approach for prediction of human work efficiency in noisy environment

In this paper, an attempt has been made to develop a fuzzy expert system for predicting the effects of sleep disturbance by noise on humans as a function of noise level, age, and duration of its occurrence. The modelling technique is based on the concept of fuzzy logic, which offers a convenient way of representing the relationships between the inputs and outputs of a system in the form of IF-THEN rules. It has been established on the basis of findings of various researchers that the effect of noise on sleep disturbance depends to a large extent on age. The middle-aged people have more probability of sleep disruption than the young people at the same noise levels. However, very little difference is found in sleep disturbance due to noise between young and old people. In addition, the duration of occurrence of noise is an important factor in determining the sleep disturbance over the limited range from few seconds to few minutes. Finally, we have compared our model results with some of the findings of researchers reported in International Journals.

A fuzzy expert system for noise-induced sleep disturbance

Solar Photovoltaic (PV) energy conversion system has drawn the tremendous attention of researchers in the past recent years. The concern related to global energy crisis and climate change threats from conventional sources of energy leads to look for alternate sources of energy. Solar energy is seen as a potential alternate source of energy as it is available globally in abundant. To develop an efficient and stable solar PV energy conversion various analysis are done by the researchers. It is analyzed at various facets like PV module characteristic and its arrangement, Power Electronics converters topologies, Maximum Power Point Tracking (MPPT) algorithm, stability of the system etc. This paper present an overview of the research going on in this area, focused on aspects mentioned above. This overview may be useful for further investigation to improve the system.

Zaheeruddin

Papers

Renewable energy management through microgrid central controller design: An approach to integrate solar, wind and biomass with battery

A neuro-fuzzy approach for prediction of human work efficiency in noisy environment

A fuzzy expert system for noise-induced sleep disturbance

Research on Solar Photovoltaic (PV) energy conversion system: An overview

Load frequency regulation by de-loaded tidal turbine power plant units using fractional fuzzy based PID droop controller