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PyHENet : a generic framework for privacy-preserving DL inference based on fully homomorphic encryption

- Chen, Qian, Yao, Lin, Wu, Yulin, Wang, Xuan, Zhang, Weizhe, Jiang, Zoe, Liu, Yang, Alazab, Mamoun

  • Authors: Chen, Qian , Yao, Lin , Wu, Yulin , Wang, Xuan , Zhang, Weizhe , Jiang, Zoe , Liu, Yang , Alazab, Mamoun
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 4th International Conference on Data Intelligence and Security, ICDIS 2022, Shenzhen, China, 24-26 August 2022, Proceedings 2022 4th International Conference on Data Intelligence and Security ICDIS 2022 p. 127-133
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  • Description: Deep learning inference provides inference service by service provider with model for client with input of personal data. Due to the huge commercial value inside, on one hand, both client's original data and inference output should be kept secret from others, even including service provider. On the other hand, service provider's model should be kept secret, especially from his competitor. Current research on privacy-preserving deep learning inference focuses on building models with specific data. This paper proposes a generic framework PyHENet of privacy-preserving deep learning inference based on Pytorch and lattice-based FHE, such that crypto library can be flexibly embedded into network. Firstly, raw data is encrypted by lattice-based FHE and uploaded to service provider. Secondly, convolutional computation over float-point ciphertext data is proposed for service provider to execute low accuracy loss inference with aided parallel method SIMD. Thirdly, inference result in ciphertext format is sent back to client for decryption. To improve efficiency, inference procedure can be further divided into two phases. All the computations during the second phase are in plaintext format with GPU acceleration, while keeping the first phase unchanged. Using the same model and parameters, the relative accuracy of PyHENet is almost 100% compared to the plaintext inference. This paper is the first to propose a general framework of neural networks for fully homomorphic cryptographic inference, and is based on mainstream deep learning frameworks, which is both secure and more conducive to development. © 2022 IEEE.

Radiation analysis of a particle curtain using polydisperse particel size eulerian granular CFD modelling

- Patel, Smitkumar, Chen, Jingling, Coventry, Joe, Lipinski, Wojciech, Kumar, Apurv

  • Authors: Patel, Smitkumar , Chen, Jingling , Coventry, Joe , Lipinski, Wojciech , Kumar, Apurv
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 7th Thermal and Fluids Engineering Conference, TFEC 2022, Las Vegas USA, 15-18 May 2022, Proceedings of the Thermal and Fluids Engineering Summer Conference Vol. 2022-May, p. 969-981
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  • Description: A solid particle receiver is a principal element in concentrated solar power technology in which falling ceramic particles in a cavity are exposed to extremely concentrated solar irradiation. Such particle receiver holds great promise in achieving high thermal efficiencies due to the possibility of reaching temperatures as high as 1000° C. To accurately model the hydrodynamics and the radiation heat transfer, it is imperative to simulate particle-gas interactions more realistically. All the particle receiver modelling till date has used only monodisperse size assumption to simplify the simulation. However, the radiation interaction with the particle curtain is greatly dependent on the size-dependent radiation properties. In the present work, we aim to model the two-dimensional mass, momentum and radiative transfer equations using a Eulerian-Eulerian multiphase granular model and the discrete ordinates model with a particle size distribution in the falling particles. Gaussian distribution is assumed as a representative size distribution spread around a mean particle size of magnitudes generally used in particle receivers (~100-500 µm). The distribution is then split into n size bands and the Eulerian granular flow is modelled for n secondary phases (up to 3 in this study) with a corresponding concentration to simulate the particle size distribution. Each particle size band was prescribed unique size dependent radiation absorption and scattering coefficient for solving the discrete ordinates radiative transfer equation. Finally, a parametric study is carried out to understand the effect of different particle sizes and their concentration on the volume fraction distribution, particle velocities and radiation absorption by the curtain inside the receiver. © 2022 Begell House Inc.. All rights reserved.

Resilience of stablecoin reserve for distributed energy trading

- Islam, Md Ezazul, Chetty, Madhu, Lim, Suryani, Chadhar, Mehmood, Islam, Syed

  • Authors: Islam, Md Ezazul , Chetty, Madhu , Lim, Suryani , Chadhar, Mehmood , Islam, Syed
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 14th IEEE PES Asia-Pacific Power and Energy Engineering Conference, APPEEC 2022, Melbourne, Australia, 20-23 November 2022, Asia-Pacific Power and Energy Engineering Conference, APPEEC Vol. 2022-November
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  • Description: For payment settlement, a blockchain-based Peer-To-Peer (P2P) energy trading requires a stable medium of exchange with little price volatility. Stablecoins, the most suitable medium of exchange, gaining concentration even from central banks. A consortium of central banks recommends complying with capital and liquidity standards for the high-quality liquid asset (HQLA) for the solvency of banks or financial institutions. Stablecoin as HQLA requires the adaption of such standards in P2P energy trading for reserve resilience. We propose a mechanism (NF90) that controls the inflow of stablecoins responding to Liquidity Coverage Ratio (LCR) for reserve resilience. Basel III accord recommends 100% of LCR. We measure the efficiency of NF90 concerning LCR as a metric. We simulate the proposed mechanism in Hyperledger Fabric as a permissioned blockchain platform for decentralisation, data storage, and smart contract. Experiments show that NF90 is the most efficient inflow control mechanism compared to other simulated mechanisms. © 2022 IEEE.

Revisiting social media in health care : a Bakhtinian carnival perspective

- Ukoha, Chukwuma, Stranieri, Andrew

  • Authors: Ukoha, Chukwuma , Stranieri, Andrew
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 Australasian Computer Science Week, ACSW 2022, Virtual, Online, 14-17 February 2022, ACM International Conference Proceeding Series p. 254-256
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  • Description: Understanding the value of social media in health care has been a conundrum. Much of the literature in this area focuses on the use of social media for promotion, with very few studies seeking to elucidate how social media yields value in health care settings. This article draws on concepts from 18th century linguist Mikhail Bahktin to explain that social media acts like a Carnival in suspension of behavioral norms, and the provision of a forum for the proliferation of diverse dialogues. As a Carnival, social media plays an important role in encouraging dialogues that would not be appropriate within other spaces in the health care system. As such, social media is playing a pivotal role in changing norms toward shared care and patient empowerment. © 2022 ACM.

Risk and resiliency assessments of renewable dominated edge of grid under high-impact low-probability events -a Review

- Surinkaew, Tossaporn, Shah, Rakibuzzaman, Islam, Syed

Security of Internet of Things devices : ethical hacking a drone and its mitigation strategies

- Karmakar, Gour, Petty, Mark, Ahmed, Hassan, Das, Rajkumar, Kamruzzaman, Joarder

  • Authors: Karmakar, Gour , Petty, Mark , Ahmed, Hassan , Das, Rajkumar , Kamruzzaman, Joarder
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022, Gold Coast, Australia, 18-20 December 2022, Proceedings of IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2022
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  • Description: Internet of Things (IoT) is enabling us to introduce cost-effective, innovative and intelligent services in business, industrial, and government application domains. Despite these huge potential benefits of IoT applications, since the backbone of IoT is Internet and IoT connects numerous heterogeneous devices, IoT is vulnerable to many different attacks and thus has been a honey pot to the cybercriminals and hackers. For this reason, the attacks against IoT devices are increasing sharply in recent years. To prevent and detect these attacks, ethical hacking of different IoT devices are of paramount importance. This is because the lesson learnt from these ethical hackings can be exploited to develop effective and robust strategies and mitigation approaches to protect IoT devices from these attacks. There exist a few ethical hacking techniques reported in the literature such as hacking Android phones, Windows XP virtual machine and a DNS rebinding attack on IoT devices. In this paper, we implement an approach for the ethical hacking of a Drone and then hijack it. As an outcome of lesson learnt, the mitigation approaches on how to reduce the hacking on a drone is presented in this paper. © 2022 IEEE.

SEeMS : advanced artificial neural networks for employee learning motivation prediction

- Sin, Audrey, Islam, Sardar, Prentice, Catherine, Xia, Feng

  • Authors: Sin, Audrey , Islam, Sardar , Prentice, Catherine , Xia, Feng
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 7th IEEE International conference for Convergence in Technology, I2CT 2022, Pune, India, 7-9 April 2022, Proceedings 2022 IEEE 7th International conference for Convergence in Technology (I2CT)
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  • Description: Employee learning motivation is vital for employee professional development and organisational success. However, worldwide statistics show that employees are generally unmotivated to learn. This study aims to examine employee learning motivation signals to determine the best-fit model for early intervention. In this paper, we present SEeMS a Smart Employee learning Motivation System to predict employee learning motivation autonomously. An Advanced Artificial Neural Networks (AANN) with a blended activation function of Sigmoid and ReLu (bSigReLu) is proposed and compared with other learning models. Experimental results demonstrate that the proposed model outperformed conventional state-of-art models. This novel study contributes to the field of organisational behaviour and data science by extending the usage of kernels and customised activation functions to solve the employee learning motivation problem. The superiority of the algorithm makes SEeMS ideal for practical deployment. According to the predictions, organisations could design better strategies to improve employee learning motivation for targeted employees. It is the first step towards achieving an eco-system of self-motivated employee learning that contributes to employee job satisfaction, performance, and well-being, indirectly contributing to employer competitiveness. © 2022 IEEE.

sGrid++ : revising simple grid based density estimator for mining outlying aspect

- Samariya, D., Ma, J., Aryal, S.

  • Authors: Samariya, D. , Ma, J. , Aryal, S.
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 23rd International Conference on Web Information Systems Engineering, WISE 2021, Biarritz, France, 1-3 November 2022, Web Information Systems Engineering – WISE 2022 23rd International Conference, Biarritz, France, November 1–3, 2022, Proceedings Vol. 13724 LNCS, p. 194-208
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  • Description: In this paper, we address the problem of outlying aspect mining, which aims to identify a set of features (subspace(s) a.k.a aspect(s)) where a given data object stands out from the rest of the data. To detect the most outlying aspect of a given data object, outlying aspect mining algorithms need to compare and rank subspaces with different dimensionality. Thus, they require a fast and dimensionally unbias scoring measure. Existing measures use density or distance to compute the outlyingness of the query in each subspace. Density and distance are dimensionally bias, i.e. density decreases as the dimension of subspace increases. To make them comparable (dimensionally unbias), Z-score normalization is used in the previous works. However, to compute Z-score normalization, we need to compute the outlyingness of each data point in each subspace, which adds significant computational overhead on top of the already expensive density or distance computation. Recently developed measure called sGrid is a simple and efficient density estimator which allows a fast systemic search. While it is efficient compared to other distance and density-based measures, it is also a dimensionally bias measure and it requires to use Z-score normalization to make it dimensionality unbiased, which makes it computationally expensive. In this paper, we propose a simpler version of sGrid called sGrid++ that is not only efficient and effective but also dimensionality unbiased. It does not require Z-score normalization. We demonstrate the effectiveness and efficiency of the proposed scoring measure in outlying aspect mining using synthetic and real-world datasets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Spam email categorization with nlp and using federated deep learning

- Ul Haq, Ikram, Black, Paul, Gondal, Iqbal, Kamruzzaman, Joarder, Watters, Paul, Kayes, A.

  • Authors: Ul Haq, Ikram , Black, Paul , Gondal, Iqbal , Kamruzzaman, Joarder , Watters, Paul , Kayes, A.
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 18th International Conference on Advanced Data Mining and Applications, ADMA 2022, Brisbane, Australia, 28-30 November 2022, Advanced Data Mining and Applications, 18th International Conference, ADMA 2022 Vol. 13726 LNAI, p. 15-27
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  • Description: Emails are the most popular and efficient communication method that makes them vulnerable to misuse. Federated learning (FL) provides a decentralized machine learning (ML) model, where a central server coordinates clients that collaboratively train a shared ML model. This paper proposes Federated Phishing Filtering (FPF) technique based on federated learning, natural language processing, and deep learning. FL for intelligent algorithms fuses trained models of ML algorithms from multiple sites for collective learning. This approach improves ML performance by utilizing large collective training data sets across the corporate client base, resulting in higher phishing email detection accuracy. FPF techniques preserve email privacy using local feature extraction on client email servers. Thus, the contents of emails do not need to be transmitted across the network or stored on third-party servers. We have applied FL and Natural Language Processing (NLP) for email phishing detection. This technique provides four training modes that perform FL without sharing email content. Our research categorizes emails as benign, spam, and phishing. Empirical evaluations with publicly available datasets show that accuracy is improved by the use of our Federated Deep Learning model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The significance and impact of winning an academic award : a study of early career academics

- Ren, Jing, Shi, Yajie, Shatte, Adrian, Kong, Xiangjie, Xia, Feng

  • Authors: Ren, Jing , Shi, Yajie , Shatte, Adrian , Kong, Xiangjie , Xia, Feng
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 22nd ACM/IEEE Joint Conference on Digital Libraries, JCDL 2022, Virtual, online, 20-24 June 2022, Proceedings of the ACM/IEEE Joint Conference on Digital Libraries
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  • Description: Academic award plays an important role in an academic's careerparticularly for early career academics. Previous studies have primarilyfocused on the impact of awards conferred to academics whoe made outstanding contributions to a specific research field, such as the Nobel Prize. In contrast, this paper aims to investigatethe effect of awards conferred to academics at an earlier careerstage, who have the potential to make a great impact in the future. We devise a metric named Award Change Factor (ACF), to evaluatethe change of a recipient's academic behavior after winningan academic award. Next, we propose a model to compare awardrecipients with academics who have similar performance beforewinning an academic award. In summary, we analyze the impact ofan award on the recipients' academic impact and their teams fromdifferent perspectives. Experimental results show that most recipientsdo have improvements in both productivity and citations afterwinning an academic award, while there is no significant impacton publication quality. In addition, receipt of an academic awardnot only expands recipients' collaboration network, but also has apositive effect on their team size. © 2022 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Theoretical study and empirical investigation of sentence analogies

- Afantenos, Stergos, Lim, Suryani, Prade, Henri, Richard, Gilles

  • Authors: Afantenos, Stergos , Lim, Suryani , Prade, Henri , Richard, Gilles
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 1st Workshop on the Interactions between Analogical Reasoning and Machine Learning at 31st International Joint Conference on Artificial Intelligence - 25th European Conference on Artificial Intelligence, IARML@IJCAI-ECAI 2022, Vienna, Austria, 23 July 2022, CEUR Workshop Proceedings Vol. 3174, p. 15-28
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  • Description: Analogies between 4 sentences, “a is to b as c is to d”, are usually defined between two pairs of sentences (a, b) and (c, d) by constraining a relation R holding between the sentences of the first pair, to hold for the second pair. From a theoretical perspective, three postulates define an analogy - one of which is the “central permutation” postulate which allows the permutation of central elements b and c. This postulate is no longer appropriate in sentence analogies since the existence of R offers no guarantee in general for the existence of some relation S such that S also holds for the pairs (a, c) and (b, d). In this paper, the “central permutation” postulate is replaced by a weaker “internal reversal” postulate to provide an appropriate definition of sentence analogies. To empirically validate the aforementioned postulate, we build a LSTM as well as baseline Random Forest models capable of learning analogies based on quadruplets. We use the Penn Discourse Treebank (PDTB), the Stanford Natural Language Inference (SNLI) and the Microsoft Research Paraphrase (MSRP) corpora. Our experiments show that our models trained on samples of analogies between (a, b) and (c, d), recognize analogies between (b, a) and (d, c) when the underlying relation is symmetrical, validating thus the formal model of sentence analogies using “internal reversal” postulate. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Third party data service providers can enhance patient-provider interactions : insights from a Delphi study

- Hashmi, Mustafa, McInnes, Angelique, Stranieri, Andrew, Sahama, Tony

  • Authors: Hashmi, Mustafa , McInnes, Angelique , Stranieri, Andrew , Sahama, Tony
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 2022 Australasian Computer Science Week, ACSW 2022, Virtual, Online, 14-17 February 2022, ACM International Conference Proceeding Series p. 224-228
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  • Description: Data sharing between financial services organisations has led to a proliferation of third party data service providers that are not parties to transactions but facilitate interactions between them by analysing, manipulating or storing data related to transactions. This has led to widespread legal, technological and sociocultural changes in that sector broadly described as Open-Banking initiatives. Third party service providers have not emerged in the healthcare sector in the same way. This study reports preliminary results of a Delphi study comprising healthcare and financial experts to explore the extent to which third party providers in healthcare is beneficial and feasible. Ensuring the quality of data service provided by third parties was seen to be a critical success factor. A causal loop model was used to describe the inter-dependent factors underpinning this factor. Further investigations to augment the model with Consumer Data Rights and validate empirically are underway. © 2022 ACM.

Ultimate pit limit optimization by computerized and manual methods for Dadiin Khar Tolgoi – 2 coal mine – a case study

- Purevdavaa, Tuvshinzaya, Khandelwal, Manoj

  • Authors: Purevdavaa, Tuvshinzaya , Khandelwal, Manoj
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: International Conference on Geotechnical challenges in Mining, Tunneling and Underground structures, ICGMTU 2021, Virtual, online, 20-21 December 2021, Lecture Notes in Civil Engineering Vol. 228, p. 97-116
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  • Description: Open-pit mine design and long-term planning are crucial parts of the mining industry. It contains the technical plan that must be followed from the beginning of mine development to mine closure. Every deposit can be mined entirely, however, many things need to be carefully considered before mining the whole deposit. An optimal pit outline is one of the most important parameters that affect both the economy and the safety of mining. Therefore, the purpose of this study is to determine the effectiveness of an optimal pit outline by using both software and hand methods based on cost and revenue. This study investigates Dadiin-Khar Tolgoi2’s optimal pit outline and design to maximize the profit of using Surpac and Whittle software. The study compares the economic gain of both entirely extracted proved (B) and probable (C) reserve mines that were designed on the Surpac software and the hand technique of determining optimal pit outline. Whittle software computed that Pit Shell – 27 of Dadiin-Khar Tolgoi – 2 is the best pit outline regarding profit and safety. Besides, entirely mined Pit outline –B and Pit outline – C gives the loss in economics both

Using analogical proportions for explanations

- Lim, Suryani, Prade, Henri, Richard, Gilles

  • Authors: Lim, Suryani , Prade, Henri , Richard, Gilles
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 15th International Conference on Scalable Uncertainty Management, SUM 2022, Paris, France, 17-19 October 2022, Scalable Uncertainty Management: 15th International Conference, SUM 2022, Paris, France, October 17-19, 2022 Proceedings Vol. 13562 LNAI, p. 309-325
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  • Description: In this article, we offer an introduction to the notion of analogical explanations. Because analogical reasoning is a widely used type of reasoning, we take the view that analogy-based explanations will be acceptable for humans. The cornerstone of the approach is the concept of analogical proportion (i.e., statements of the form “a is to b as c is to d”), comparing 2 pairs of items. Analogical proportions are not simply based on similarity but also involve differences between items. The approach applies to the explanation of the label of an item in a repository, whether the couple (item, label) belongs to a sample of a given population or the label is predicted via an algorithm. The output can be in terms of abductive/factual explanations (answering a “why?” question and providing examples having the same label) or contrastive/counter-factual (answering a “why not?” question and providing examples having a different label). For preliminary experiments, we build Boolean data sets where relevant attributes are known. Our results show that analogical proportion-based explanations can be effective. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Validation framework of bayesian networks in asset management decision-making

- Morey, Stephen, Chattopadhyay, Gopinath, Larkins, Jo-ann

  • Authors: Morey, Stephen , Chattopadhyay, Gopinath , Larkins, Jo-ann
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: International Congress and Workshop on Industrial AI, IAI 2021, Virtual online, 6-7 October 2021, published in Lecture Notes in Mechanical Engineering p. 360-369
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  • Description: Capital-intensive industries are under increasing pressure from capital constraints to extend the life of long-life assets and to defer asset renewals. Assets in most of those industries have complex life-cycle management challenges in aspects of design, manufacture, maintenance and service contracts, the usage environment, and changes in support personnel over the asset life. A significant challenge is the availability and quality of relevant data for informed decision-making in assuring reliability, availability and safety. There is a need for better-informed maintenance decisions and cost-effective interventions in managing the risk and assuring performance of those assets. Bayesian networks have been considered in asset management applications in recent years for addressing these challenges, by modelling of reliability, maintenance decisions, life extension and prognostics, across a wide range of technological domains of complex assets. However, models of long-life assets are challenging to validate, particularly due to issues with data scarcity and quality. A literature review on Bayesian networks in asset management in this paper shows that there is a need for further work in this area. This paper discusses the issues and challenges of validation of Bayesian network models in asset management and draws on findings from literature research to propose a preliminary validation framework for Bayesian network models in life-cycle management applications of capital-intensive long-life assets. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Vulnerability assessment framework for a smart grid

- Rashed, Muhammad, Kamruzzaman, Joarder, Gondal, Iqbal, Islam, Syed

  • Authors: Rashed, Muhammad , Kamruzzaman, Joarder , Gondal, Iqbal , Islam, Syed
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 4th IEEE Global Power, Energy and Communication Conference, GPECOM 2022, Cappadocia, Turkey, 14-17 June 2022, Proceedings - 2022 IEEE 4th Global Power, Energy and Communication Conference, GPECOM 2022 p. 449-454
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  • Description: The increasing demand for the interconnected IoT based smart grid is facing threats from cyber-attacks due to inherent vulnerability in the smart grid network. There is a pressing need to evaluate and model these vulnerabilities in the network to avoid cascading failures in power systems. In this paper, we propose and evaluate a vulnerability assessment framework based on attack probability for the protection and security of a smart grid. Several factors were taken into consideration such as the probability of attack, propagation of attack from a parent node to child nodes, effectiveness of basic metering system, Kalman estimation and Advanced Metering Infrastructure (AMI). The IEEE-300 bus smart grid was simulated using MATPOWER to study the effectiveness of the proposed framework by injecting false data injection attacks (FDIA); and studying their propagation. Our results show that the use of severity assessment standards such as Common Vulnerability Scoring System (CVSS), AMI measurements and Kalman estimates were very effective for evaluating the vulnerability assessment of smart grid in the presence of FDIA attack scenarios. © 2022 IEEE.

A design of haptic hand exoskeleton for virtual reality applications

- Le, Duy, Nguyen, Linh

A Fast and Reliable Blocked Bus Bar Protection Scheme Leveraging on Sampled Value and GOOSE Protection based on IEC 61850 Architecture

- Kumar, Shantanu, Abu-Siada, Ahmed, Das, Narottam, Islam, Syed

  • Authors: Kumar, Shantanu , Abu-Siada, Ahmed , Das, Narottam , Islam, Syed
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 31st Australasian Universities Power Engineering Conference, AUPEC 2021, Virtual, Online 26 to 30 September 2021, Proceedings of 2021 31st Australasian Universities Power Engineering Conference, AUPEC 2021
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  • Description: Substation Automation System (SAS) is in a matured state and promises to replace conventional protection scheme. It has created a new paradigm in its application for future smart grids. The multifunction features of IEC 61850 facilitate this technology to reliably monitor, protect and control critical assets. Mostly agree, the intelligent electronic devices (IEDs) and substation automation features have the same effect as 'html' had in developing web technology on IT system in 1990's. However, in order to be acceptable completely, this technology needs to gain confidence of end user, it needs to be researched further using multivendor equipment and experimented in a network comprising of complex secondary protection schemes. In this paper, a practical Blocked Busbar Protection (BBP) scheme have been implemented and tested for a single bus bar scheme, leveraging on Sampled Values (SV) and Generic Object Oriented Substation Event (GOOSE) at laboratory to prove its reliability and improvement in technology. This experiment has been carried out for three scenarios in a simulated substation network related to a process plant industry. Furthermore, this laboratory-based experiment with multivendor IEDs evaluated performance of an Ethernet and FO based process bus network to validate overall performance of BBP protection scheme. It concluded with a comparison between digital and conventional protection scheme with recommendation for future applications. © 2021 IEEE.

A secured real-time IoMT application for monitoring isolated COVID-19 patients using edge computing

- Balasubramanian, Venki, Sulthana, Rehena, Stranieri, Andrew, Manoharan, G., Arora, Teena, Srinivasan, Ram, Mahalakshmi, K., Menon, Varun

A study on the use of machine learning methods to improve reciprocating compressor reliability via torque tailoring

- Lu, Kui, Sultan, Ibrahim, Phung, Truong

  • Authors: Lu, Kui , Sultan, Ibrahim , Phung, Truong
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2021, Ballarat, Australia, 12-15 December 2021, 2021 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2021
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  • Description: Reciprocating compressors have found popularity in applications where compressed air is required at high pressure levels with moderate flow rates. The mechanical drives used for these compressors are based on the traditional slider-crank linkage which, despite its simplicity, does not lend itself to optimization effort aimed at improving the compressor reliability and performance. The work presented in this paper adopts the notion that the mechanical reliability of the compressor drive is limited by the level and cyclical variability of the loads transmitted through its members and the effectiveness of its cooling system. In the paper, machine learning methods will be employed to craft an objective function suitable to use in a Bayesian optimization effort undertaken to produce a more reliable compressor drive. A numerical example is presented to prove the validity of the presented method and its suitability for use in real life compressor designs. © 2021 IEEE.