A literature review of the positive displacement compressor : current challenges and future opportunities
- Authors: Lu, Kui , Sultan, Ibrahim , Phung, Truong
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Energies Vol. 16, no. 20 (2023), p.
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- Description: Positive displacement compressors are essential in many engineering systems, from domestic to industrial applications. Many studies have been devoted to providing more insights into the workings and proposing solutions for performance improvements of these machines. This study aims to present a systematic review of published research on positive displacement compressors of various geometrical structures. This paper discusses the literature on compressor topics, including leakage, heat transfer, friction and lubrication, valve dynamics, port characteristics, and capacity control strategies. Moreover, the current status of the application of machine learning methods in positive displacement compressors is also discussed. The challenges and opportunities for future work are presented at the end of the paper. © 2023 by the authors.
Applications of machine learning and deep learning in antenna design, optimization, and selection : a review
- Authors: Sarker, Nayan , Podder, Prajoy , Mondal, M. , Shafin, Sakib , Kamruzzaman, Joarder
- Date: 2023
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 11, no. (2023), p. 103890-103915
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- Description: This review paper provides an overview of the latest developments in artificial intelligence (AI)-based antenna design and optimization for wireless communications. Machine learning (ML) and deep learning (DL) algorithms are applied to antenna engineering to improve the efficiency of the design and optimization processes. The review discusses the use of electromagnetic (EM) simulators such as computer simulation technology (CST) and high-frequency structure simulator (HFSS) for ML and DL-based antenna design, which also covers reinforcement learning (RL)-bases approaches. Various antenna optimization methods including parallel optimization, single and multi-objective optimization, variable fidelity optimization, multilayer ML-assisted optimization, and surrogate-based optimization are discussed. The review also covers the AI-based antenna selection approaches for wireless applications. To support the automation of antenna engineering, the data generation technique with computational electromagnetics software is described and some useful datasets are reported. The review concludes that ML/DL can enhance antenna behavior prediction, reduce the number of simulations, improve computer efficiency, and speed up the antenna design process. © 2013 IEEE.
Attacks on self-driving cars and their countermeasures : a survey
- Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Jolfaei, Alireza , Das, Rajkumar
- Date: 2020
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 8, no. (2020), p. 207308-207342
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- Description: Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-To-Vehicle (V2V), Vehicle-To-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle's operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-Attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-Attack. We also provide further research directions to improve the security issues associated with self-driving cars. © 2013 IEEE.
Blending big data analytics : review on challenges and a recent study
- Authors: Amalina, Fairuz , Targio Hashem, Ibrahim , Azizul, Zati , Fong, Ang , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 8, no. (2020), p. 3629-3645
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- Description: With the collection of massive amounts of data every day, big data analytics has emerged as an important trend for many organizations. These collected data can contain important information that may be key to solving wide-ranging problems, such as cyber security, marketing, healthcare, and fraud. To analyze their large volumes of data for business analyses and decisions, large companies, such as Facebook and Google, adopt analytics. Such analyses and decisions impact existing and future technology. In this paper, we explore how big data analytics is utilized as a technique for solving problems of complex and unstructured data using such technologies as Hadoop, Spark, and MapReduce. We also discuss the data challenges introduced by big data according to the literature, including its six V's. Moreover, we investigate case studies of big data analytics on various techniques of such analytics, namely, text, voice, video, and network analytics. We conclude that big data analytics can bring positive changes in many fields, such as education, military, healthcare, politics, business, agriculture, banking, and marketing, in the future. © 2013 IEEE.
Energy harvesting in underwater acoustic wireless sensor networks : design, taxonomy, applications, challenges and future directions
- Authors: Khan, Anwar , Imran, Muhammad , Alharbi, Abdullah , Mohamed, Ehab , Fouda, Mostafa
- Date: 2022
- Type: Text , Journal article , Review
- Relation: IEEE Access Vol. 10, no. (2022), p. 134606-134622
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- Description: In underwater acoustic wireless sensor networks (UAWSNs), energy harvesting either enhances the lifetime of a network by increasing the battery power of sensor nodes or ensures battery-less operation of nodes. This, in effect, results in sustainable and reliable operation of the network deployed for various underwater applications. This work provides a survey of the energy harvesting techniques for UAWSNs. Our work is unique than the existing work on underwater energy harvesting in that it includes state-of-the art techniques designed in the last decade. It analyzes every harvesting scheme in terms of its main idea, merits, demerits and the extent of the harvested power (energy). The description of the merits results in selection of the suitable scheme for the suitable underwater applications. The demerits of the addressed schemes provide an insight to their future enhancement and improvement. Moreover, the harvested techniques are classified into various categories depending upon the involved energy harvesting mechanism and compared based on the maximum and minimum amount of harvested power, which helps in selection of the suitable category keeping in view the power budget of an underwater network before deployment. The challenges in energy harvesting and in UAWSNs are described to provide an insight to them and to address them for further enhancement in the harvested extent. Finally, research directions are specified for future investigation. © 2013 IEEE.
Small-signal stability and resonance perspectives in microgrid : a review
- Authors: Krismanto, Awan , Mithulananthan, Nadarajah , Shah, Rakibuzzaman , Setiadi, Herlambang , Islam, Md Rabiul
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Energies Vol. 16, no. 3 (2023), p.
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- Description: The microgrid (MG) system is a controlled and supervised power system consisting of renewable energy (RE)-based distributed generation (DG) units, loads, and energy storage. The MG can be operated autonomously or while connected to the grid. Higher intermittencies and uncertainties can be observed in MGs compared to the conventional power system, which is the possible source of small-signal stability in MG systems. It can be seen as disturbances around the stable operating point, which potentially lead to the small-signal instability problem within MGs. Small-signal instability issues also emerge due to the lack of damping torque in the MG. The integration of power electronic devices and complex control algorithms within MGs introduces novel challenges in terms of small-signal stability and possible resonances. The occurrence of interaction in a low- or no-inertia system might worsen the stability margin, leading to undamped oscillatory instability. The interaction within the MG is characterized by various frequency ranges, from low-frequency subsynchronous oscillation to high-frequency ranges around the harmonic frequencies. This study presents an overview of the dynamic model, possible sources of small-signal instability problems, and resonance phenomena in MGs. The developed models of MG, including structure, converter-based power generation, and load and control algorithms, are briefly summarized to provide the context of MG system dynamics. A comprehensive critical review of the previous research, including small-signal stability and resonance phenomenon for MGs, is also provided. Finally, key future research areas are recommended. © 2023 by the authors.
The lithium-ion battery recycling process from a circular economy perspective—a review and future directions
- Authors: Sheth, Rahil , Ranawat, Narendra , Chakraborty, Ayon , Mishra, Rajesh , Khandelwal, Manoj
- Date: 2023
- Type: Text , Journal article , Review
- Relation: Energies Vol. 16, no. 7 (2023), p.
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- Description: Ever since the introduction of lithium-ion batteries (LIBs) in the 1970s, their demand has increased exponentially with their applications in electric vehicles, smartphones, and energy storage systems. To cope with the increase in demand and the ensuing environmental effects of excessive mining activities and waste production, it becomes crucial to explore ways of manufacturing LIBs from the resources that have already been extracted from nature. It is possible by promoting the re-usage, refurbishing, and recycling of the batteries and their constituent components, rethinking the fundamental design of devices using these batteries, and introducing the circular economy model in the battery industry. This paper through a literature review provides the current state of CE adoption in the lithium-ion battery industry. The review suggests that the focus is mostly on recycling at this moment in the battery industry, and a further understanding of the process is needed to better adapt to other CE practices such as reuse, remanufacture, refurbishment, etc. The paper also provides the steps involved in the recycling process and, through secondary case studies, shows how some of the industries are currently approaching battery recycling. Thus, this paper, through review and secondary cases, helps us to understand the current state of LIB recycling and CE adoption. © 2023 by the authors.