Multi-level supervisory emergency control for operation of remote area microgrid clusters
- Authors: Batool, Munira , Shahnia, Farhad , Islam, Syed
- Date: 2019
- Type: Text , Journal article
- Relation: Journal of Modern Power Systems and Clean Energy Vol. 7, no. 5 (Sep 2019), p. 1210-1228
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- Description: Remote and regional areas are usually supplied by isolated and self-sufficient electricity systems, which are called as microgrids (MGs). To reduce the overall cost of electricity production, MGs rely on non-dispatchable renewable sources. Emergencies such as overloading or excessive generation by renewable sources can result in a substantial voltage or frequency deviation in MGs. This paper presents a supervisory controller for such emergencies. The key idea is to remedy the emergencies by optimal internal or external support. A multi-level controller with soft, intermedial and hard actions is proposed. The soft actions include the adjustment of the droop parameters of the sources and the controlling of the charge/discharge of energy storages. The intermedial action is exchanging power with neighboring MGs, which is highly probable in large remote areas. As the last remedying resort, curtailing loads or renewable sources are assumed as hard actions. The proposed controller employs an optimization technique consisting of certain objectives such as reducing power loss in the tie-lines amongst MGs and the dependency of an MG to other MGs, as well as enhancing the contribution of renewable sources in electricity generation. Minimization of the fuel consumption and emissions of conventional generators, along with frequency and voltage deviation, is the other desired objectives. The performance of the proposal is evaluated by several numerical analyses in MATLAB (R).
Market model for clustered microgrids optimisation including distribution network operations
- Authors: Batool, Munira , Islam, Syed , Shahnia, Farhad
- Date: 2019
- Type: Text , Journal article
- Relation: IET Generation, Transmission and Distribution Vol. 13, no. 22 (2019), p. 5139-5150
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- Description: This paper proposes a market model for the purpose of optimisation of clustered but sparse microgrids (MGs). The MGs are connected with the market by distribution networks for the sake of energy balance and to overcome emergency situations. The developed market structure enables the integration of virtual power plants (VPPs) in energy requirement of MGs. The MGs, internal service providers (ISPs), VPPs and distribution network operator (DNO) are present as distinct entities with individual objective of minimum operational cost. Each MG is assumed to be present with a commitment to service its own loads prior to export. Thus an optimisation problem is formulated with the core objective of minimum cost of operation, reduced network loss and least DNO charges. The formulated problem is solved by using heuristic optimization technique of Genetic Algorithm. Case studies are carried out on a distribution system with multiple MGs, ISP and VPPs which illustrates the effectiveness of the proposed market optimisation strategy. The key objective of the proposed market model is to coordinate the operation of MGs with the requirements of the market with the help of the DNO, without decreasing the economic efficiency for the MGs nor the distribution network. © The Institution of Engineering and Technology 2019.
Toward a substation automation system based on IEC 61850
- Authors: Kumar, Shantanu , Abu-Siada, Ahmed , Das, Narottam , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 3 (2021), p. 1-16
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- Description: With the global trend to digitalize substation automation systems, International Electro technical Commission 61850, a communication protocol defined by the International Electrotechnical Commission, has been given much attention to ensure consistent communication and integration of substation high-voltage primary plant assets such as instrument transformers, circuit breakers and power transformers with various intelligent electronic devices into a hierarchical level. Along with this transition, equipment of primary plants in the switchyard, such as non-conventional instrument transformers, and a secondary system including merging units are expected to play critical roles due to their fast-transient response over a wide bandwidth. While a non-conventional instrument transformer has advantages when compared with the conventional one, extensive and detailed performance investigation and feasibility studies are still required for its full implementation at a large scale within utilities, industries, smart grids and digital substations. This paper is taking one step forward with respect to this aim by employing an optimized network engineering tool to evaluate the performance of an Ethernet-based network and to validate the overall process bus design requirement of a high-voltage non-conventional instrument transformer. Furthermore, the impact of communication delay on the substation automation system during peak traffic is investigated through a detailed simulation analysis. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
On semiregularity of mappings
- Authors: Cibulka, Radek , Fabian, Marian , Kruger, Alexander
- Date: 2019
- Type: Text , Journal article
- Relation: Journal of Mathematical Analysis and Applications Vol. 473, no. 2 (2019), p. 811-836
- Relation: http://purl.org/au-research/grants/arc/DP160100854
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- Description: There are two basic ways of weakening the definition of the well-known metric regularity property by fixing one of the points involved in the definition. The first resulting property is called metric subregularity and has attracted a lot of attention during the last decades. On the other hand, the latter property which we call semiregularity can be found under several names and the corresponding results are scattered in the literature. We provide a self-contained material gathering and extending the existing theory on the topic. We demonstrate a clear relationship with other regularity properties, for example, the equivalence with the so-called openness with a linear rate at the reference point is shown. In particular cases, we derive necessary and/or sufficient conditions of both primal and dual type. We illustrate the importance of semiregularity in the convergence analysis of an inexact Newton-type scheme for generalized equations with not necessarily differentiable single-valued part. © 2019 Elsevier Inc.
Extension of ZVS region of series-series WPT systems by an auxiliary variable inductor for improving efficiency
- Authors: Li, Yong , Liu, Shunpan , Zhu, Xia , Hu, Jiefeng , Zhang, Min , Mai, Ruikun , He, Zhengyou
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Power Electronics Vol. 36, no. 7 (2021), p. 7513-7525
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- Description: To maintain a stable output voltage under various operating conditions without introducing extra dc/dc converters, phase-shift (PS) control is usually adopted for wireless power transfer (WPT) systems. By using this method, however, zero-voltage switching (ZVS) operation cannot be guaranteed, especially in light-load conditions. To achieve high efficiency and reduce electromagnetic interference, it is significant for WPT systems to achieve ZVS operation of all switching devices in the whole operation range. In this article, an auxiliary variable inductor, of which the equivalent inductance can be controlled by adjusting the dc current in its auxiliary winding, is designed for series-series-compensated WPT systems under PS control to mitigate the loss arising from hard switching. As a result, a wide ZVS operation range of all switching devices can be achieved. A laboratory prototype is built to verify the theoretical analysis. The experimental results show that, under load and magnetic coupling variations, ZVS operation at fixed operation frequency as well as a constant dc output voltage can be maintained. Compared to the conventional method with only PS control, the proposed WPT can achieve higher overall efficiency in a wider load range owing to the wide ZVS operation range. © 1986-2012 IEEE.
Malware variant identification using incremental clustering
- Authors: Black, Paul , Gondal, Iqbal , Bagirov, Adil , Moniruzzaman, Md
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics Vol. 10, no. 14 (2021), p.
- Relation: http://purl.org/au-research/grants/arc/DP190100580
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A unifying approach to robust convex infinite optimization duality
- Authors: Dinh, Nguyen , Goberna, Miguel , López, Marco , Volle, Michel
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Optimization Theory and Applications Vol. 174, no. 3 (2017), p. 650-685
- Relation: http://purl.org/au-research/grants/arc/DP160100854
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- Description: This paper considers an uncertain convex optimization problem, posed in a locally convex decision space with an arbitrary number of uncertain constraints. To this problem, where the uncertainty only affects the constraints, we associate a robust (pessimistic) counterpart and several dual problems. The paper provides corresponding dual variational principles for the robust counterpart in terms of the closed convexity of different associated cones.
A smart priority-based traffic control system for emergency vehicles
- Authors: Karmakar, Gour , Chowdhury, Abdullahi , Kamruzzaman, Joarder , Gondal, Iqbal
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Sensors Journal Vol. 21, no. 14 (2021), p. 15849-15858
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- Description: Unwanted events on roads, such as incidents and increased traffic jams, can cause human lives and economic loss. For efficient incident management, it is essential to send Emergency Vehicles (EVs) to the incident place as quickly as possible. To reduce incidence clearance time, several approaches exist to provide a clear pathway to EVs mainly fitted with RFID sensors in the urban areas. However, they neither assign priority to the EVs based on the type and severity of an incident nor consider the effect on other on-road traffic. To address this issue, in this paper, we introduce an Emergency Vehicle Priority System (EVPS) by determining the priority level of an EV based on the type and the severity of an incident, and estimating the number of necessary signal interventions while considering the impact of those interventions on the traffic in the roads surrounding the EV's travel path. We present how EVPS determines the priority code and a new algorithm to estimate the number of green signal interventions to attain the quickest incident response while concomitantly reducing impact on others. A simulation model is developed in Simulation of Urban Mobility (SUMO) using the real traffic data of Melbourne, Australia, captured by various sensors. Results show that our system recommends appropriate number of intervention that can reduce emergency response time significantly. © 2001-2012 IEEE.
Multi-variate data fusion technique for reducing sensor errors in intelligent transportation systems
- Authors: Manogaran, Gunasekaran , Balasubramanian, Venki , Rawal, Bharat , Saravanan, Vijayalakshmi , Montenegro-Marin, Carlos
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Sensors Journal Vol. 21, no. 14 (2021), p. 15564-15573
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- Description: Connected vehicles in intelligent transportation system (ITS) scenario rely on environmental data for supporting user-centric applications along the driving time. Sensors equipped in the vehicles are responsible for accumulating data from the environment, followed by the fusion process. Such fusion process provides accurate and stable data required for the applications in a recurrent manner. In order to enhance the data fusion of connected vehicles, this article introduces multi-variate data fusion (MVDF) technique. This technique is competent in handling asynchronous and discrete data from the environment and streamlining them into continuous and delay-less inputs for the applications. The process of data fusion is aided through least square regression learning to determine the errors in different time instances. The indefinite and definite data fusion instances are differentiated using this regression model to identify the errors in fore-hand. Besides, the differentiation relies on the application run-time interval to progress data fusion within the same or extended time instance and data slots. In this manner the differentiation along with the error identification is regular until the application required data is met. The performance of this technique is verified using network simulator experiments for the metrics error, data utilization ratio, and computation time. The results show that this technique improves data utilization under controlled time and fewer errors. © 2001-2012 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Venki Balasubramanian” is provided in this record**.
Industrial electronics education : past, present, and future perspectives
- Authors: Lucia, Oscar , Martins, Joao , Ibrahim, Yousef , Umetani, Kazuhiro , Gomes, Luis
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Industrial Electronics Magazine Vol. 15, no. 1 (2021), p. 140-154
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- Description: Industrial electronics (IE) covers a wide range of technologies and applications, being a key enabling technology for numerous industrial, domestic, and biomedical uses, among others. In this context, IE education has become a relevant and challenging topic for society and industry. This article covers its evolution and state-of-The-Art methodologies and provides an overall view of its status around the world. Finally, future trends and challenges in IE education are discussed. © 2007-2011 IEEE. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Yousef Ibrahim” is provided in this record**
Enhanced profitability of photovoltaic plants by utilizing cryptocurrency-based mining load
- Authors: Eid, Bilal , Islam, Md Rabiul , Shah, Rakibuzzaman , Nahid, Abdullah , Kouzani, Abbas , Mahmud, M.
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Applied Superconductivity Vol. 31, no. 8 (2021), p.
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- Description: The grid connected photovoltaic (PV) power plants (PVPPs) are booming nowadays. The main problem facing the PV power plants deployment is the intermittency which leads to instability of the grid. In order to stabilize the grid, either energy storage device - mainly batteries - or a power curtailment technique can be used. The additional cost on utilizing batteries make it not preferred solution, because it leads to a drop in the return on investment (ROI) of the project. A good alternative, is using a customized load (such as; cryptocurrency-based loads) which consumes the surplus energy. This paper investigating the usage of a customized load - cryptocurrency mining rig - to create an added value for the owner of the plant and increase the ROI of the project. These devices are widely used to perform the required calculations for validating the transactions on the network of the Blockchain. A comparison between the ROI of the mining rig and the battery have been conducted in this study. Based on this study the mining rig has superior ROI of 7.7% - in the case with the lowest ROI - compared to 4.5% for battery. Moreover, an improved controlling strategy is developed to combine both the battery and mining rig in the same system. The developed strategy is able to keep the profitability as high as possible during the fluctuation of the mining network. © 2002-2011 IEEE.
ADMM-based adaptive sampling strategy for nonholonomic mobile robotic sensor networks
- Authors: Le, Viet-Anh , Nguyen, Linh , Nghiem, Truong
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Sensors Journal Vol. 21, no. 13 (2021), p. 15369-15378
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- Description: This paper discusses the adaptive sampling problem in a nonholonomic mobile robotic sensor network for efficiently monitoring a spatial field. It is proposed to employ Gaussian process to model a spatial phenomenon and predict it at unmeasured positions, which enables the sampling optimization problem to be formulated by the use of the log determinant of a predicted covariance matrix at next sampling locations. The control, movement and nonholonomic dynamics constraints of the mobile sensors are also considered in the adaptive sampling optimization problem. In order to tackle the nonlinearity and nonconvexity of the objective function in the optimization problem we first exploit the linearized alternating direction method of multipliers (L-ADMM) method that can effectively simplify the objective function, though it is computationally expensive since a nonconvex problem needs to be solved exactly in each iteration. We then propose a novel approach called the successive convexified ADMM (SC-ADMM) that sequentially convexify the nonlinear dynamic constraints so that the original optimization problem can be split into convex subproblems. It is noted that both the L-ADMM algorithm and our SC-ADMM approach can solve the sampling optimization problem in either a centralized or a distributed manner. We validated the proposed approaches in 1000 experiments in a synthetic environment with a real-world dataset, where the obtained results suggest that both the L-ADMM and SC-ADMM techniques can provide good accuracy for the monitoring purpose. However, our proposed SC-ADMM approach computationally outperforms the L-ADMM counterpart, demonstrating its better practicality. © 2001-2012 IEEE.
Matching algorithms : fundamentals, applications and challenges
- Authors: Ren, Jing , Xia, Feng , Chen, Xiangtai , Liu, Jiaying , Sultanova, Nargiz
- Date: 2021
- Type: Text , Journal article , Review
- Relation: IEEE Transactions on Emerging Topics in Computational Intelligence Vol. 5, no. 3 (2021), p. 332-350
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- Description: Matching plays a vital role in the rational allocation of resources in many areas, ranging from market operation to people's daily lives. In economics, the term matching theory is coined for pairing two agents in a specific market to reach a stable or optimal state. In computer science, all branches of matching problems have emerged, such as the question-answer matching in information retrieval, user-item matching in a recommender system, and entity-relation matching in the knowledge graph. A preference list is the core element during a matching process, which can either be obtained directly from the agents or generated indirectly by prediction. Based on the preference list access, matching problems are divided into two categories, i.e., explicit matching and implicit matching. In this paper, we first introduce the matching theory's basic models and algorithms in explicit matching. The existing methods for coping with various matching problems in implicit matching are reviewed, such as retrieval matching, user-item matching, entity-relation matching, and image matching. Furthermore, we look into representative applications in these areas, including marriage and labor markets in explicit matching and several similarity-based matching problems in implicit matching. Finally, this survey paper concludes with a discussion of open issues and promising future directions in the field of matching. © 2017 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Jing Ren, Xia Feng, Nargiz Sultanova" is provided in this record**
Cross-compiler bipartite vulnerability search
- Authors: Black, Paul , Gondal, Iqbal
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 11 (2021), p.
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- Description: Open-source libraries are widely used in software development, and the functions from these libraries may contain security vulnerabilities that can provide gateways for attackers. This paper provides a function similarity technique to identify vulnerable functions in compiled programs and proposes a new technique called Cross-Compiler Bipartite Vulnerability Search (CCBVS). CCBVS uses a novel training process, and bipartite matching to filter SVM model false positives to improve the quality of similar function identification. This research uses debug symbols in programs compiled from open-source software products to generate the ground truth. This automatic extraction of ground truth allows experimentation with a wide range of programs. The results presented in the paper show that an SVM model trained on a wide variety of programs compiled for Windows and Linux, x86 and Intel 64 architectures can be used to predict function similarity and that the use of bipartite matching substantially improves the function similarity matching performance. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Industrial IoT based condition monitoring for wind energy conversion system
- Authors: Hossain, Md Liton , Abu-Siada, Ahmed , Muyeen, S. , Hasan, Mubashwar , Rahman, Md Momtazur
- Date: 2021
- Type: Text , Journal article
- Relation: CSEE Journal of Power and Energy Systems Vol. 7, no. 3 (2021), p. 654-664
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- Description: Wind energy has been identified as the second dominating source in the world renewable energy generation after hydropower. Conversion and distribution of wind energy has brought technology revolution by developing the advanced wind energy conversion system (WECS) including multilevel inverters (MLIs). The conventional rectifier produces ripples in their output waveforms while the MLI suffers from voltage balancing issues across the DC-link capacitor. This paper proposes a simplified proportional integral (PI)-based space vector pulse width modulation (SVPWM) to minimize the output waveform ripples, resolve the voltage balancing issue and produce better-quality output waveforms. WECS experiences various types of faults particularly in the DC-link capacitor and switching devices of the power converter. These faults, if not detected and rectified at an early stage, may lead to catastrophic failures to the WECS and continuity of the power supply. This paper proposes a new algorithm embedded in the proposed PI-based SVPWM controller to identify the fault location in the power converter in real time. Since most wind power plants are located in remote areas or offshore, WECS condition monitoring needs to be developed over the internet of things (IoT) to ensure system reliability. In this paper, an industrial IoT algorithm with an associated hardware prototype is proposed to monitor the condition of WECS in the real-time environment. © 2015 CSEE.
Detecting outlier patterns with query-based artificially generated searching conditions
- Authors: Yu, Shuo , Xia, Feng , Sun, Yuchen , Tang, Tao , Yan, Xiaoran , Lee, Ivan
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 1 (2021), p. 134-147
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- Description: In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this article, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between the nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined in a real-world academic network using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs and is robust to the choice of similarity measures. © 2014 IEEE.
An efficient 3-D model for remaining wall thicknesses of cast iron pipes in nondestructive testing
- Authors: Nguyen, Linh , Miro, Jaime
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Sensors Letters Vol. 4, no. 7 (2020), p.
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- Description: Over 50% of global pipes have been made of cast iron, and most of them are aging. In order to effectively estimate possibilities of their failures, which is paramount for efficiently managing asset infrastructures, it requires the remaining wall thickness (RWT) of a pipe to be known. In fact, RWT of the cast iron pipes can be primarily measured by the magnetism based nondestructive testing technologies though they are quite slow. To speed up the inspection process, it is proposed to sense RWT of a part of a pipe and then employ a model to predict RWT in the rest. Thus, this letter introduces a 3-D model to efficiently represent RWT of a pipe given measurements collected intermittently on the pipe's surface. The proposed model first transforms 3-D cylindrical coordinates to 3-D Cartesian coordinates before modeling RWT by Gaussian processes (GP). The transformation allows GP to work properly on RWT data gathered on a cylindrical pipe and effectively predict RWT at unmeasured locations. Moreover, periodicity of RWT along circumference of the pipe is naturally integrated. The effectiveness of the proposed approach is demonstrated by implementation in two real life inservice aging cast iron pipes, where the obtained results are highly promising. © 2017 IEEE.
Dynamic voltage signature of large scale PV enriched streesed power system
- Authors: Alzahrani, Saeed , Shah, Rakibuzzaman , Mithulananthan, Nadarajah , Sode-Yome, Arthit
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020; Bangkok, Thailand; 15th-18th September 2020 p. 275-280
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- Description: Renewable power generations including flexible demand and energy storage systems leverage significant changes in network operation. Thereby, power systems with high renewable penetration manifest deteriorated resilience to disturbances. Hence, the stable operation of the system could be affected. With a paradigm shift, dynamic voltage stability becomes one of the major concerns for the transmission system operators (TSOs). Predicting the dynamic voltage signature for the transmission system with high penetration of renewables is essential to assist in selecting appropriate corrective control. This paper utilized a comprehensive assessment framework to identify the dynamic voltage signature of the power system with PV and various loads. The voltage recovery index has been chosen as the quantifiable index to extricate the dynamic voltage signature. The applicability of the proposed framework is discussed using simulation studies on the IEEE-39 bus test system. © 2020 IEEE.
Efficient evaluation of remaining wall thickness in corroded water pipes using pulsed Eddy current data
- Authors: Nguyen, Linh , Miro, Jaime Valls
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Sensors Journal Vol. 20, no. 23 (2020), p. 14465-14473
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- Description: In order to analyse failures of an ageing water pipe, some methods such as the loss-of-section require remaining wall thickness (RWT) along the pipe to be fully known, which can be measured by the magnetism based non-destructive evaluation sensors though they are practically slow due to the magnetic penetrating process. That is, fully measuring RWT at every location in a water pipe is not really practical if RWT inspection causes disruption of water supply to customers. Thus, this paper proposes a new data prediction approach that can increase amount of RWT data of a corroded water pipe collected in a given period of time by only measuring RWT on a part (e.g. 20%) of the total pipe surface area and then employing the measurements to predict RWT at unmeasured area. It is proposed to utilize a marginal distribution to convert the non-Gaussian RWT measurements to the standard normally distributed data, which can then be input into a 3-dimensional Gaussian process model for efficiently predicting RWT at unmeasured locations on the pipe. The proposed approach was implemented in two real-life in-service pipes, and the obtained results demonstrate its practicality. © 2001-2012 IEEE.
Machine learning for 5G security : architecture, recent advances, and challenges
- Authors: Afaq, Amir , Haider, Noman , Baig, Muhammad , Khan, Komal , Imran, Muhammad , Razzak, Imran
- Date: 2021
- Type: Text , Journal article
- Relation: Ad Hoc Networks Vol. 123, no. (2021), p.
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- Description: The granularization of crucial network functions implementation using software-centric, and virtualized approaches in 5G networks have brought forth unprecedented security challenges in general and privacy concerns. Moreover, these software components’ premature deployment and compromised supply chain put the individual network components at risk and have a ripple effect for the rest of the network. Some of the novel threats to 5G assets include tampering in identity and access management, supply-chain poisoning, masquerade and bot attacks, loop-holes in source codes. Machine learning (ML) in this context can help to provide heavily dynamic and robust security mechanisms for the software-centric architecture of 5G Networks. ML models’ development and implementation also rely on programmable environments; hence, they can play a vital role in designing, modelling, and automating efficient security protocols. This article presents the threat landscape across 5G networks and discusses the feasibility and architecture of different ML-based models to counter these threats. Also, we present the architecture for automated threat intelligence using cooperative and coordinated ML to secure 5G assets and infrastructure. We also present the summary of closely related existing works along with future research challenges. © 2021 Elsevier B.V.