6G wireless systems : a vision, architectural elements, and future directions
- Authors: Khan, Latif , Yaqoob, Ibrar , Imran, Muhammad , Han, Zhu , Hong, Choong
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 147029-147044
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- Description: Internet of everything (IoE)-based smart services are expected to gain immense popularity in the future, which raises the need for next-generation wireless networks. Although fifth-generation (5G) networks can support various IoE services, they might not be able to completely fulfill the requirements of novel applications. Sixth-generation (6G) wireless systems are envisioned to overcome 5G network limitations. In this article, we explore recent advances made toward enabling 6G systems. We devise a taxonomy based on key enabling technologies, use cases, emerging machine learning schemes, communication technologies, networking technologies, and computing technologies. Furthermore, we identify and discuss open research challenges, such as artificial-intelligence-based adaptive transceivers, intelligent wireless energy harvesting, decentralized and secure business models, intelligent cell-less architecture, and distributed security models. We propose practical guidelines including deep Q-learning and federated learning-based transceivers, blockchain-based secure business models, homomorphic encryption, and distributed-ledger-based authentication schemes to cope with these challenges. Finally, we outline and recommend several future directions. © 2013 IEEE.
A blockchain-based deep-learning-driven architecture for quality routing in wireless sensor networks
- Authors: Khan, Zahoor , Amjad, Sana , Ahmed, Farwa , Almasoud, Abdullah , Imran, Muhammad , Javaid, Nadeem
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Access Vol. 11, no. (2023), p. 31036-31051
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- Description: Over the past few years, great importance has been given to wireless sensor networks (WSNs) as they play a significant role in facilitating the world with daily life services like healthcare, military, social products, etc. However, heterogeneous nature of WSNs makes them prone to various attacks, which results in low throughput, and high network delay and high energy consumption. In the WSNs, routing is performed using different routing protocols like low-energy adaptive clustering hierarchy (LEACH), heterogeneous gateway-based energy-aware multi-hop routing (HMGEAR), etc. In such protocols, some nodes in the network may perform malicious activities. Therefore, four deep learning (DL) techniques and a real-time message content validation (RMCV) scheme based on blockchain are used in the proposed network for the detection of malicious nodes (MNs). Moreover, to analyse the routing data in the WSN, DL models are trained on a state-of-the-art dataset generated from LEACH, known as WSN-DS 2016. The WSN contains three types of nodes: sensor nodes, cluster heads (CHs) and the base station (BS). The CHs after aggregating the data received from the sensor nodes, send it towards the BS. Furthermore, to overcome the single point of failure issue, a decentralized blockchain is deployed on CHs and BS. Additionally, MNs are removed from the network using RMCV and DL techniques. Moreover, legitimate nodes (LNs) are registered in the blockchain network using proof-of-authority consensus protocol. The protocol outperforms proof-of-work in terms of computational cost. Later, routing is performed between the LNs using different routing protocols and the results are compared with original LEACH and HMGEAR protocols. The results show that the accuracy of GRU is 97%, LSTM is 96%, CNN is 92% and ANN is 90%. Throughput, delay and the death of the first node are computed for LEACH, LEACH with DL, LEACH with RMCV, HMGEAR, HMGEAR with DL and HMGEAR with RMCV. Moreover, Oyente is used to perform the formal security analysis of the designed smart contract. The analysis shows that blockchain network is resilient against vulnerabilities. © 2013 IEEE.
A blockchain-based solution for enhancing security and privacy in smart factory
- Authors: Wan, Jafu , Li, Jiapeng , Imran, Muhammad , Li, Di
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 15, no. 6 (2019), p. 3652-3660
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- Description: Through the Industrial Internet of Things (IIoT), a smart factory has entered the booming period. However, as the number of nodes and network size become larger, the traditional IIoT architecture can no longer provide effective support for such enormous system. Therefore, we introduce the Blockchain architecture, which is an emerging scheme for constructing the distributed networks, to reshape the traditional IIoT architecture. First, the major problems of the traditional IIoT architecture are analyzed, and the existing improvements are summarized. Second, we introduce a security and privacy model to help design the Blockchain-based architecture. On this basis, we decompose and reorganize the original IIoT architecture to form a new multicenter partially decentralized architecture. Then, we introduce some relative security technologies to improve and optimize the new architecture. After that we design the data interaction process and the algorithms of the architecture. Finally, we use an automatic production platform to discuss the specific implementation. The experimental results show that the proposed architecture provides better security and privacy protection than the traditional architecture. Thus, the proposed architecture represents a significant improvement of the original architecture, which provides a new direction for the IIoT development. © 2005-2012 IEEE.
A comparative study of two embodiments of the limaçon rotary compressor based on theoretical modelling of apex seal dynamics and leakage
- Authors: Lu, Kui , Sultan, Ibrahim , Phung, Truong
- Date: 2023
- Type: Text , Journal article
- Relation: International Journal of Refrigeration Vol. 145, no. (2023), p. 467-480
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- Description: As an emerging technology, the limaçon rotary compressor possesses great potential for fluid-processing applications. However, the technology and associated cost required to fabricate the limaçon machine could sometimes be beyond the capability of some manufacturers. To reduce the production cost, circolimaçon embodiment whose rotor and housing are constructed of circular arcs has been proposed. This paper is intended to investigate the viability of the circolimaçon embodiment of limaçon technology based on sealing performance. A nonlinear three-degree of freedom model is presented to describe the dynamic behaviour of the apex seal during the machine operation. Additionally, the leakage through the seal-housing gap is formulated by considering the inertia and viscous effects on the flow. A numerical illustration is offered to compare the performance of the circolimaçon embodiment with that of the limaçon-to-limaçon (L2L) type machine at different pressure ratios and operating speeds. The effect of limaçon aspect ratio on the apex seal dynamics is also investigated. Based on the results, it is found that the circolimaçon embodiment exhibits comparable performance to the L2L-type machine, despite having more significant seal vibrations. The differences in the volumetric and isentropic efficiencies between the two machines are found within 8% and 3%, respectively. Additionally, it is also discovered that the circolimaçon compressor with a small capacity undergoes lower level of seal dynamics, suggesting a better machine reliability. © 2022
A critical analysis of mobility management related issues of wireless sensor networks in cyber physical systems
- Authors: Al-Muhtadi, Jalal , Qiang, Ma , Zeb, Khan , Chaudhry, Junaid , Imran, Muhammad
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Access Vol. 6, no. (2018), p. 16363-16376
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- Description: Mobility management has been a long-standing issue in mobile wireless sensor networks and especially in the context of cyber physical systems its implications are immense. This paper presents a critical analysis of the current approaches to mobility management by evaluating them against a set of criteria which are essentially inherent characteristics of such systems on which these approaches are expected to provide acceptable performance. We summarize these characteristics by using a quadruple set of metrics. Additionally, using this set we classify the various approaches to mobility management that are discussed in this paper. Finally, the paper concludes by reviewing the main findings and providing suggestions that will be helpful to guide future research efforts in the area. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**
A data reporting protocol with revocable anonymous authentication for edge-assisted intelligent transport systems
- Authors: Wang, Yanping , Wang, Xiaofen , Dai, Hong-Ning , Zhang, Xiaosong , Imran, Muhammad
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 19, no. 6 (2023), p. 7835-7847
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- Description: Intelligent Transport Systems (ITS) have received growing attention recently driven by technical advances in Industrial Internet of Vehicles (IIoV). In IIoV, vehicles report traffic data to management infrastructures to achieve better ITS services. To ensure security and privacy, many anonymous authentication-enabled data reporting protocols are proposed. However, these protocols usually require a large number of preloaded pseudonyms or involve a costly and irrevocable group signature. Thus, they are not ready for realistic deployment due to large storage overhead, expensive computation costs, or absence of malicious users' revocation. To address these issues, we present a novel data reporting protocol for edge-assisted ITS in this paper, where the traffic data is sent to distributed edge nodes for local processing. Specifically, we propose a new anonymous authentication scheme fine-tuned to fulfill the needs of vehicular data reporting, which allows authenticated vehicles to report unlimited unlinkable messages to edge nodes without huge pseudonyms download and storage costs. Moreover, we designed an efficient certificate update scheme based on a bivariate polynomial function. In this way, malicious vehicles can be revoked with time complexity O(1). The security analysis demonstrates that our protocol satisfies source authentication, anonymity, unlinkability, traceability, revocability, nonframeability, and nonrepudiation. Further, extensive simulation results show that the performance of our protocol is greatly improved since the signature size is reduced by at least 8%, the computation costs in message signing and verification are reduced by at least 56% and 67%, respectively, and the packet loss rate is reduced by at least 14%. © 2005-2012 IEEE.
A deep learning model based on concatenation approach for the diagnosis of brain tumor
- Authors: Noreen, Neelum , Palaniappan, Sellappan , Qayyum, Abdul , Ahmad, Iftikhar , Imran, Muhammad , Shoaib, M.uhammad
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 55135-55144
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- Description: Brain tumor is a deadly disease and its classification is a challenging task for radiologists because of the heterogeneous nature of the tumor cells. Recently, computer-aided diagnosis-based systems have promised, as an assistive technology, to diagnose the brain tumor, through magnetic resonance imaging (MRI). In recent applications of pre-trained models, normally features are extracted from bottom layers which are different from natural images to medical images. To overcome this problem, this study proposes a method of multi-level features extraction and concatenation for early diagnosis of brain tumor. Two pre-trained deep learning models i.e. Inception-v3 and DensNet201 make this model valid. With the help of these two models, two different scenarios of brain tumor detection and its classification were evaluated. First, the features from different Inception modules were extracted from pre-trained Inception-v3 model and concatenated these features for brain tumor classification. Then, these features were passed to softmax classifier to classify the brain tumor. Second, pre-trained DensNet201 was used to extract features from various DensNet blocks. Then, these features were concatenated and passed to softmax classifier to classify the brain tumor. Both scenarios were evaluated with the help of three-class brain tumor dataset that is available publicly. The proposed method produced 99.34 %, and 99.51% testing accuracies respectively with Inception-v3 and DensNet201 on testing samples and achieved highest performance in the detection of brain tumor. As results indicated, the proposed method based on features concatenation using pre-trained models outperformed as compared to existing state-of-the-art deep learning and machine learning based methods for brain tumor classification. © 2013 IEEE.
A fault-tolerant cascaded switched-capacitor multilevel inverter for domestic applications in smart grids
- Authors: Akbari, Ehsan , Teimouri, Ali , Saki, Mojtaba , Rezaei, Mohammad , Hu, Jiefeng , Band, Shahab , Pai, Hao-Ting , Mosavi, Amir
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 110590-110602
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- Description: Cascaded multilevel inverters (MLIs) generate an output voltage using series-connected power modules that employ standard configurations of low-voltage components. Each module may employ one or more switched capacitors to double or quadruple its input voltage. The higher number of switched capacitors and semiconductor switches in MLIs compared to conventional two-level inverters has led to concerns about overall system reliability. A fault-tolerant design can mitigate this reliability issue. If one part of the system fails, the MLI can continue its planned operation at a reduced level rather than the entire system failing, which makes the fault tolerance of the MLI particularly important. In this paper, a novel fault location technique is presented that leads to a significant reduction in fault location detection time based on the reliability priority of the components of the proposed fault-tolerant switched capacitor cascaded MLI (CSCMLI). The main contribution of this paper is to reduce the number of MLI switches under fault conditions while operating at lower levels. The fault-tolerant inverter requires fewer switches at higher reliability, and the comparison with similar MLIs shows a faster dynamic response of fault detection and reduced fault location detection time. The experimental results confirm the effectiveness of the presented methods applied in the CSCMLI. Also, all experimental data including processor code, schematic, PCB, and video of CSCMLI operation are attached. © 2013 IEEE.
A federated learning-based license plate recognition scheme for 5G-enabled Internet of vehicles
- Authors: Kong, Xiangjie , Wang, Kailai , Hou, Mingliang , Hao, Xinyu , Shen, Guojiang , Chen, Xin , Xia, Feng
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 17, no. 12 (Dec 2021), p. 8523-8530
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- Description: License plate is an essential characteristic to identify vehicles for the traffic management, and thus, license plate recognition is important for Internet of Vehicles. Since 5G has been widely covered, mobile devices are utilized to assist the traffic management, which is a significant part of Industry 4.0. However, there have always been privacy risks due to centralized training of models. Also, the trained model cannot be directly deployed on the mobile device due to its large number of parameters. In this article, we propose a federated learning-based license plate recognition framework (FedLPR) to solve these problems. We design detection and recognition model to apply in the mobile device. In terms of user privacy, data in individuals is harnessed on their mobile devices instead of the server to train models based on federated learning. Extensive experiments demonstrate that FedLPR has high accuracy and acceptable communication cost while preserving user privacy.
A holistic power management strategy of microgrids based on model predictive control and particle swarm optimization
- Authors: Shan, Yinghao , Hu, Jiefeng , Liu, Huashan
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 18, no. 8 (2022), p. 5115-5126
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- Description: Power control and optimization are both crucial for the proper operation of a microgrid. However, in existing research, they are usually studied separately. Active and reactive powers are either maintained to constant values at device level or optimized at system level without considering frequency and voltage control of distributed converters. In this article, a holistic power control and optimization strategy is proposed for microgrids. Specifically, a model predictive control incorporated with the droop method is developed at device level to achieve load sharing and flexible power dispatching among distributed energy resources, which is feasible for both islanded and grid-connected modes. In addition, an evolutionary particle swarm optimization algorithm is designed at system level to generate the optimal active and reactive power setpoints, which are then sent to the device level for controlling inverters. The proposed power optimization scheme is able to mitigate voltage deviations and minimize the operational cost of the microgrid. Comprehensive case studies and real-time simulator test are provided to demonstrate the feasibility and efficacy of the proposed power control and optimization strategy. © 2005-2012 IEEE.
A hybrid computing solution and resource scheduling strategy for edge computing in smart manufacturing
- Authors: Li, Xiaomin , Wan, Jiafu , Dai, Hong-Ning , Imran, Muhammad , Xia, Min , Celesti, Antonio
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Informatics Vol. 15, no. 7 (2019), p. 4225-4234
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- Description: At present, smart manufacturing computing framework has faced many challenges such as the lack of an effective framework of fusing computing historical heritages and resource scheduling strategy to guarantee the low-latency requirement. In this paper, we propose a hybrid computing framework and design an intelligent resource scheduling strategy to fulfill the real-time requirement in smart manufacturing with edge computing support. First, a four-layer computing system in a smart manufacturing environment is provided to support the artificial intelligence task operation with the network perspective. Then, a two-phase algorithm for scheduling the computing resources in the edge layer is designed based on greedy and threshold strategies with latency constraints. Finally, a prototype platform was developed. We conducted experiments on the prototype to evaluate the performance of the proposed framework with a comparison of the traditionally-used methods. The proposed strategies have demonstrated the excellent real-time, satisfaction degree (SD), and energy consumption performance of computing services in smart manufacturing with edge computing. © 2005-2012 IEEE.
A hybrid modulation control for wireless power transfer systems to improve efficiency under light-load conditions
- Authors: Li, Yong , Sun, Wenjun , Zhu, Xiao , Hu, Jiefeng
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Industrial Electronics Vol. 69, no. 7 (2022), p. 6870-6880
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- Description: Traditional wireless power transfer (WPT) systems usually adopt the triple-phase-shift control method to maintain a constant output voltage, track the maximum system efficiency point (MEPT), and achieve zero-voltage-switching (ZVS) operation for various applications. However, these three targets are achieved at the cost of high reactive power on both primary and secondary sides, especially under light-load conditions, leading to low efficiency. This has become one of the challenges that hinder a further deployment of WPT technologies in practice. To address this vital problem, in this article, how the reactive power lowers the system efficiency is revealed based on a mathematical model established. Then, a hybrid modulation control strategy based on a proper selection between the full-bridge and half-bridge modes of the inverter and active rectifier is developed. An experimental prototype is constructed to verify the effectiveness of the proposed control method. Experimental results show that the proposed method can reduce the reactive power, maintain a constant output voltage, and realize the MEPT and ZVS operation, with high efficiencies up to 94.29% in a wide load range. © 1982-2012 IEEE.
A joint scheduling and power control scheme for hybrid I2V/V2V networks
- Authors: Nguyen, Bach , Ngo, Duy , Dao, Minh , Duong, Quang-Thang , Okada, Minoru
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Vehicular Technology Vol. 69, no. 12 (2020), p. 15668-15681
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- Description: In automotive infotainment systems, vehicles using the applications are serviced via continuous infrastructure-to-vehicle (I2V) communications. Additionally, the I2V communications can be combined with vehicle-to-vehicle (V2V) connectivity owing to the small area covered by road side units (RSUs). However, dozens of vehicles have to compete for limited bandwidth when they request service simultaneously in the covered area. In this paper, we propose a joint scheduling and power control scheme for I2V and V2V links in the RSUs' coverage range. Mapping the I2V and V2V links to tuple-links, we formulate a mixed-integer nonlinear programming (MINLP) problem where frequency scheduler and power controller for those tuple-links are jointly designed. Then, we employ the delayed column generation technique and the transmission pattern definition to decompose the MINLP problem into a transmission pattern scheduling problem, as well as a power control problem. Therein, the transmission pattern scheduling problem is solved by linear programming while a greedy power control algorithm is developed. Simulation results with practical parameter settings show that our proposed scheme outperforms several conventional schemes in terms of service disruption and achieved throughput while maintaining throughput fairness among the requesting vehicles. In particular, a high channel number, a small power level number, and a large buffer size at the requesting vehicles are shown to be helpful for our proposed scheme. © 1967-2012 IEEE.
A lightweight federated learning based privacy preserving B5G pandemic response network using unmanned aerial vehicles: A proof-of-concept
- Authors: Nasser, Nasser , Fadlullah, Zubair , Fouda, Mostafa , Ali, Asmaa , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Computer Networks Vol. 205, no. (2022), p.
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- Description: The concept of an intelligent pandemic response network is gaining momentum during the current novel coronavirus disease (COVID-19) era. A heterogeneous communication architecture is essential to facilitate collaborative and intelligent medical analytics in the fifth generation and beyond (B5G) networks to intelligently learn and disseminate pandemic-related information and diagnostic results. However, such a technique raises privacy issues pertaining to the health data of the patients. In this paper, we envision a privacy-preserving pandemic response network using a proof-of-concept, aerial–terrestrial network system serving mobile user entities/equipment (UEs). By leveraging the unmanned aerial vehicles (UAVs), a lightweight federated learning model is proposed to collaboratively yet privately learn medical (e.g., COVID-19) symptoms with high accuracy using the data collected by individual UEs using ambient sensors and wearable devices. An asynchronous weight updating technique is introduced in federated learning to avoid redundant learning and save precious networking as well as computing resources of the UAVs/UEs. A use-case where an Artificial Intelligence (AI)-based model is employed for COVID-19 detection from radiograph images is presented to demonstrate the effectiveness of our proposed approach. © 2021 Elsevier B.V.
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.
A mesoscale modelling approach coupling SBFEM, continuous damage phase-field model and discrete cohesive crack model for concrete fracture
- Authors: Yu, Kelai , Yang, Zhenjun , Li, Hui , Ooi, Ean Tat , Li, Shangming , Liu, GuoHua
- Date: 2023
- Type: Text , Journal article
- Relation: Engineering Fracture Mechanics Vol. 278, no. (2023), p.
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- Description: This study develops an innovative numerical approach for simulating complex mesoscale fracture in concrete. In this approach, the concrete meso-structures are generated using a random aggregate generation and packing algorithm. Each aggregate is modelled by a single scaled boundary finite element method (SBFEM) based polygon with the boundary discretized only. The damage and fracture in the mortar is simulated by the continuous damage phase-field regularized cohesive zone model (PF-CZM), and the aggregate-mortar interfaces are modelled by zero-thickness cohesive interface elements (CIEs) with nonlinear softening separation-traction laws. This new approach thus takes full advantages of different methods, including the semi-analytical accuracy and high flexibility in mesh generation and transition of SBFEM, the mesh and length-scale independence of PF-CZM, and the ease-of-use of CIEs in modelling discrete interfacial fracture. These advantages are demonstrated by successful simulations of a few 2D and 3D benchmark examples in mode-I and mixed-mode fracture. © 2022 Elsevier Ltd
A multi-objective deep reinforcement learning framework
- Authors: Nguyen, Thanh , Nguyen, Ngoc , Vamplew, Peter , Nahavandi, Saeid , Dazeley, Richard , Lim, Chee
- Date: 2020
- Type: Text , Journal article
- Relation: Engineering Applications of Artificial Intelligence Vol. 96, no. (2020), p.
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- Description: This paper introduces a new scalable multi-objective deep reinforcement learning (MODRL) framework based on deep Q-networks. We develop a high-performance MODRL framework that supports both single-policy and multi-policy strategies, as well as both linear and non-linear approaches to action selection. The experimental results on two benchmark problems (two-objective deep sea treasure environment and three-objective Mountain Car problem) indicate that the proposed framework is able to find the Pareto-optimal solutions effectively. The proposed framework is generic and highly modularized, which allows the integration of different deep reinforcement learning algorithms in different complex problem domains. This therefore overcomes many disadvantages involved with standard multi-objective reinforcement learning methods in the current literature. The proposed framework acts as a testbed platform that accelerates the development of MODRL for solving increasingly complicated multi-objective problems. © 2020 Elsevier Ltd
A new current limiting and overload protection scheme for distributed inverters in microgrids under grid faults
- Authors: Li, Zilin , Hu, Jiefeng , Chan, Ka Wing
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Industry Applications Vol. 57, no. 6 (2021), p. 6362-6374
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- Description: Unlike a synchronous generator that could withstand a large overcurrent, an inverter-based distributed generation (DG) has low thermal inertia, and the inverter is likely damaged by overcurrents during grid faults. In this article, a new strategy, namely positive-And negative-sequence limiting with stability enhanced P-f droop control (PNSL-SEPFC), is proposed to limit the output currents and active power of droop-controlled inverters in islanded microgrids. This strategy is easy to implement in the inverter controller and does not require any fault detection. Inverter stability is analyzed mathematically, which gives guidelines to design the parameters of the PNSL-SEPFC strategy. PSCAD/EMTDC simulation based on a four-DG microgrid shows that the proposed PNSL-SEPFC can limit inverter output currents and powers with better performance under both symmetrical and asymmetrical faults. Furthermore, hardware experiments demonstrate that the proposed PNSL-SEPFC can ensure the inverters riding through grid faults safely and stably. (A video of experimental waveforms is attached.). © 1972-2012 IEEE.
A new hybrid cascaded switched-capacitor reduced switch multilevel inverter for renewable sources and domestic loads
- Authors: Rezaei, Mohammad , Nayeripour, Majid , Hu, Jiefeng , Band, Shahab , Mosavi, Amir , Khooban, Mohammad-Hassan
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Access Vol. 10, no. (2022), p. 14157-14183
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- Description: This multilevel inverter type summarizes an output voltage of medium voltage based on a series connection of power cells employing standard configurations of low-voltage components. The main problems of cascaded switched-capacitor multilevel inverters (CSCMLIs) are the harmful reverse flowing current of inductive loads, the large number of switches, and the surge current of the capacitors. As the number of switches increases, the reliability of the inverter decreases. To address these issues, a new CSCMLI is proposed using two modules containing asymmetric DC sources to generate 13 levels. The main novelty of the proposed configuration is the reduction of the number of switches while increasing the maximum output voltage. Despite the many similarities, the presented topology differs from similar topologies. Compared to similar structures, the direction of some switches is reversed, leading to a change in the direction of current flow. By incorporating the lowest number of semiconductors, it was demonstrated that the proposed inverter has the lowest cost function among similar inverters. The role of switched-capacitor inrush current in the selection of switch, diode, and DC source for inverter operation in medium and high voltage applications is presented. The inverter performance to supply the inductive loads is clarified. Comparison of the simulation and experimental results validates the effectiveness of the proposed inverter topology, showing promising potentials in photovoltaic, buildings, and domestic applications. A video demonstrating the experimental test, and all manufacturing data are attached. © 2013 IEEE.
A novel collaborative IoD-assisted VANET approach for coverage area maximization
- Authors: Ahmed, Gamil , Sheltami, Tarek , Mahmoud, Ashraf , Imran, Muhammad , Shoaib, Muhammad
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Access Vol. 9, no. (2021), p. 61211-61223
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- Description: Internet of Drones (IoD) is an efficient technique that can be integrated with vehicular ad-hoc networks (VANETs) to provide terrestrial communications by acting as an aerial relay when terrestrial infrastructure is unreliable or unavailable. To fully exploit the drones' flexibility and superiority, we propose a novel dynamic IoD collaborative communication approach for urban VANETs. Unlike most of the existing approaches, the IoD nodes are dynamically deployed based on current locations of ground vehicles to effectively mitigate inevitable isolated cars in conventional VANETs. For efficiently coordinating IoD, we model IoD to optimize coverage based on the location of vehicles. The goal is to obtain an efficient IoD deployment to maximize the number of covered vehicles, i.e., minimize the number of isolated vehicles in the target area. More importantly, the proposed approach provides sufficient interconnections between IoD nodes. To do so, an improved version of succinct population-based meta-heuristic, namely Improved Particle Swarm Optimization (IPSO) inspired by food searching behavior of birds or fishes flock, is implemented for IoD assisted VANET (IoDAV). Moreover, the coverage, received signal quality, and IoD connectivity are achieved by IPSO's objective function for optimal IoD deployment at the same time. We carry out an extensive experiment based on the received signal at floating vehicles to examine the proposed IoDAV performance. We compare the results with the baseline VANET with no IoD (NIoD) and Fixed IoD assisted (FIoD). The comparisons are based on the coverage percentage of the ground vehicles and the quality of the received signal. The simulation results demonstrate that the proposed IoDAV approach allows finding the optimal IoD positions throughout the time based on the vehicle's movements and achieves better coverage and better quality of the received signal by finding the most appropriate IoD position compared with NIoD and FIoD schemes. © 2013 IEEE.