In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

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Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Annual Energy Outlook 2008 With Projections to 2030

Applications of Artificial Intelligence and Machine learning in smart cities

This paper provides a comprehensive review and analyses on the state-of-the-art and future trends for high-power conductive on-board chargers (OBCs) for electric vehicles. To provide a global context, a summary of global charging standards and electric vehicle (EV) related trends are presented, which demonstrates momentum toward the OBCs with higher power rating. High-power OBCs are either unidirectional or bidirectional, and they have either an integrated or non-integrated system architecture. Non-integrated high-power OBCs are studied both from industry and academia, and the former are used to illustrate the current state of the art. The latter are classified on the basis of the converter design approach, studied for their principle of operation, and compared over power density, weight, efficiency, and other metrics. In addition to non-integrated OBCs, recent advancements in propulsion-machine integrated OBC solutions are also presented. Other integrated OBC techniques, such as system integration with the EV's auxiliary power module and wireless charging systems, are also discussed. Finally, future charging strategies and functionalities in charging infrastructures are addressed, and global OBC trends are summarized.

Global Trends in High-Power On-Board Chargers for Electric Vehicles

This paper conducts a comprehensive study on the application of big data and machine learning in the electrical power grid introduced through the emergence of the next-generation power system-the smart grid (SG). Connectivity lies at the core of this new grid infrastructure, which is provided by the Internet of Things (IoT). This connectivity, and constant communication required in this system, also introduced a massive data volume that demands techniques far superior to conventional methods for proper analysis and decision-making. The IoT-integrated SG system can provide efficient load forecasting and data acquisition technique along with cost-effectiveness. Big data analysis and machine learning techniques are essential to reaping these benefits. In the complex connected system of SG, cyber security becomes a critical issue; IoT devices and their data turning into major targets of attacks. Such security concerns and their solutions are also included in this paper. Key information obtained through literature review is tabulated in the corresponding sections to provide a clear synopsis; and the findings of this rigorous review are listed to give a concise picture of this area of study and promising future fields of academic and industrial research, with current limitations with viable solutions along with their effectiveness.

/pdf/application-of-big-data-and-machine-learning-in-smart-grid-1taystle2u.pdf

Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

Electric vehicles (EV), including Battery Electric Vehicle (BEV), Hybrid Electric Vehicle (HEV), Plug-in Hybrid Electric Vehicle (PHEV), Fuel Cell Electric Vehicle (FCEV), are becoming more commonplace in the transportation sector in recent times. As the present trend suggests, this mode of transport is likely to replace internal combustion engine (ICE) vehicles in the near future. Each of the main EV components has a number of technologies that are currently in use or can become prominent in the future. EVs can cause significant impacts on the environment, power system, and other related sectors. The present power system could face huge instabilities with enough EV penetration, but with proper management and coordination, EVs can be turned into a major contributor to the successful implementation of the smart grid concept. There are possibilities of immense environmental benefits as well, as the EVs can extensively reduce the greenhouse gas emissions produced by the transportation sector. However, there are some major obstacles for EVs to overcome before totally replacing ICE vehicles. This paper is focused on reviewing all the useful data available on EV configurations, battery energy sources, electrical machines, charging techniques, optimization techniques, impacts, trends, and possible directions of future developments. Its objective is to provide an overall picture of the current EV technology and ways of future development to assist in future researches in this sector.

https://www.preprints.org/manuscript/201705.0090/v1/download

A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development

Utility electric grid is expected to face some of the major challenges modern power generation, and distribution systems will pose in the years to come. In this paper, a complete overview of those challenges is presented, for the first time, with proper detailed explanation and their implication. Smart grid security, power system generation and distribution side complications, integration of renewable energy systems, storage devices, modern equipment, and technologies make modern-day power generation and transmission more complex than ever. Thus, the utility grid requires major renovations and upgraded protection schemes to adapt to these changes. The required solutions with discussions are also presented in this paper. In addition, the challenges are classified according to their severity, and the solutions that are best suited to mitigate each challenge are also identified. Challenges of the modern utility grid and their prospective solutions are expected to open up a lot of opportunities for researchers around the world, who work toward improving the utility by adopting latest technologies.

Utility Grid: Present Challenges and Their Potential Solutions

Vehicle fleet electrification is regarded as one major pathway toward achieving energy independence and reducing air pollution and greenhouse gas emissions. Compared to light-duty and medium-duty v...

Fuad Un-Noor

Papers

A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development

Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development

Utility Grid: Present Challenges and Their Potential Solutions

Feasibility of Operating a Heavy-Duty Battery Electric Truck Fleet for Drayage Applications: