Showing items 1 - 20 of 2094

Your selections:

Analysing maintenance and renewal decision of sealed roads at city council in australia

- Shrestha, Kishan, Chattopadhyay, Gopi

  • Authors: Shrestha, Kishan , Chattopadhyay, Gopi
  • Date: 2024
  • Type: Text , Conference paper
  • Relation: 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Lulea, Sweden, 13-15 June 2023, International Congress and Workshop on Industrial AI and eMaintenance 2023 Conference proceedings p. 291-301
  • Full Text: false
  • Reviewed:
  • Description: Roads are one of the major physical infrastructures of Hepburn Shire Council (HSC) as of all other local councils. Every year HSC allocates and spends huge amount of budget on roads for maintenance and renewal. The road performance condition level has been the major priority for roads renewal selection. However, other criteria are under-considered, and there are gaps in significant analysis of the relation between roads age, condition, risk, and cost. In this study, decision-making framework or tool has developed using multi criteria technique (MCT) and analytic Hierarchy Process (AHP) for single objective optimisation i.e., to provide an agreed level of service optimising Maintenance and Renewal cost or improve the condition subjected to annual budget. This study adopted decision criteria as per community and council needs, by developing a model for criteria selection. Additionally, this study analysed the adopted HSC maintenance strategies, condition monitoring systems, performance conditions of the roads, and operational and renewal budget of HSC. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Detection of anomalies and explanation in cybersecurity

- Samariya, Durgesh, Ma, Jiangang, Aryal, Sunil, Zhao, Xiaohui

Knowledge graph completion via subgraph topology augmentation

- Huang, Haufei, Ding, Feng, Zhang, Fengyi, Wang, Yingbo, Peng, Ciyuan, Shehzad, Ahsan, Lei, Qihang, Cong, Lili, Yu, Shuo

Process reliability analysis applied for continual improvement of large-scale alumina refineries

- Don, R. Welandage, Chattopadhyay, Gopi, Kamruzzaman, Joarder

  • Authors: Don, R. Welandage , Chattopadhyay, Gopi , Kamruzzaman, Joarder
  • Date: 2024
  • Type: Text , Conference paper
  • Relation: 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Lulea, Sweden, 13-15 June 2023, International Congress and Workshop on Industrial AI and eMaintenance 2023 Conference proceedings p. 665-677
  • Full Text: false
  • Reviewed:
  • Description: Large-scale alumina refineries use strategic planning to forecast production plans for short, medium, and long term operational decisions. However, actual production deviates from the forecast due to reasons within Supplier, Input, Process, Output and Contractor (SIPOC) related variations including unplanned downtimes, issues with supply chain disruptions, availability of staff and demand fluctuations due to numerous factors including environmental changes, if any. The unreliable production process results in lost revenue and adversely affects the corporate image. This paper presents a statistical approach applying the Weibull model to identify the causes of production deviation and find improvement opportunities for reducing costs and risks while enhancing performance. An illustrative example from a chemical alumina refinery plant in Australia is presented. The various steps used in the analysis are discussed in this paper using illustrative example where production data is analysed and compared for diverse options of interventions for a robust and effective method for managers to better understand the gaps for monitoring and assuring plant performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

A brief guide to multi-objective reinforcement learning and planning JAAMAS track

- Hayes, Conor, Bargiacchi, Eugenio, Källström, Johan, Macfarlane, Matthew, Reymond, Mathieu, Verstraeten, Timothy, Zintgraf, Luisa, Dazeley, Richard, Heintz, Frederik, Howley, Enda, Irissappane, Aathirai, Mannion, Patrick, Nowé, Ann, Ramos, Gabriel, Restelli, Marcello, Vamplew, Peter, Roijers, Diederik

A case for causal loop diagrams to model electronic health records ecosystems

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

  • Authors: Hashmi, Mustafa , McInnes, Angelique , Sahama, Tony , Stranieri, Andrew
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 Australasian Computer Science Week, ACSW 2023, Melbourne, Australia, 31 January-3 February 2023, ACSW '23: Proceedings of the 2023 Australasian Computer Science Week p. 238-239
  • Full Text: false
  • Reviewed:
  • Description: Causal loop diagrams (CLD) that emerged from systems thinking disciplines have been used to simulate complex inter-dependencies between causal factors in diverse phenomena. This paper highlights a process for generating a casual loop diagrams to represent the quality of electronic health record (EHR) ecosystem in a medical context. The quality inherent in the use of electronic health records for specific clinical purposes is taken to depend on factors including data integrity, reliability, relevance, timeliness and completeness. By improving the electronic health record ecosystem quality, health care providers can enhance their data sharing practices, and personalised patient care, while reducing the probabilities of medical errors. Ultimately the CLD can be used to run multiple simulations for several clinical case scenarios to understand the impact of various case phenomena on the quality of the electronic health record ecosystem. © 2023 ACM.

A hybrid PWM strategy for SMES integrated grid-feeding transformerless PV Inverters

- Mondal, Sudipto, Biswas, Shuvra, Bin Islam, Md Sabbir, Islam, Md Rabiul, Shah, Rakibuzzaman

A new tour on the subdifferential of the Supremum function

- Hantoute, Abderrahim, López-Cerdá, Marco

A Novel Common Grounded Type 1-\varphi Five-Level Boost PV Inverter with Reduced Device Count

- Ardashir, Jaber, Ghadim, Hadi, Heydari, Dorsa, Hu, Jiefeng

  • Authors: Ardashir, Jaber , Ghadim, Hadi , Heydari, Dorsa , Hu, Jiefeng
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 8th International Conference on Technology and Energy Management, ICTEM 2023, Babol, Iran, 8-9 February 2023, 8th International Conference on Technology and Energy Management, ICTEM 2023
  • Full Text: false
  • Reviewed:
  • Description: Common-grounded multi-level inverters with the input voltage boost ability are more common for grid-tied PV system applications. In this paper, a new five-level inverter topology is introduced that can generate a five-level voltage at the output stage, statically increase the input voltage, fully transfer the reactive power, eliminate the leakage current due to common-grounded capability, reduce the number of the power electronic components, self-balancing capability of switched capacitors voltage based on operation modes, and equality of switched capacitors voltage. The operation of the proposed inverter along with its comparison with other similar inverter topologies and the simulation results of the proposed inverter under different loads are demonstrates in this paper. The results indicate that the performance of the proposed inverter in the grid-connected PV system is best than other compared topologies. Also, experimental results with a prototype have been presented, which validates the simulation results and proves the applicability of the proposed inverter in grid-tied PV systems. © 2023 IEEE.

A robust ensemble regression model for reconstructing genetic networks

- Gamage, Hasini, Chetty, Madhu, Lim, Suryani, Hallinan, Jennifer, Nguyen, Huy

  • Authors: Gamage, Hasini , Chetty, Madhu , Lim, Suryani , Hallinan, Jennifer , Nguyen, Huy
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 International Joint Conference on Neural Networks, IJCNN 2023 Vol. 2023-June
  • Full Text: false
  • Reviewed:
  • Description: Genetic networks contain important information about biological processes, including regulatory relationships and gene-gene interactions. Numerous methods, using high-dimensional gene expression data have been developed to capture these interactions. These gene expression data, generated using high-throughput technologies, are prone to noise. However, most existing network inference methods are unable to cope with noisy data, making genetic network reconstruction challenging. In this paper, we propose a novel ensemble regression model combining quantile regression and cross-validated Ridge regression, RidgeCV, to infer interactions from noisy gene expression data. The application of quantile regression to GRN inference is novel, and its design makes it appropriate for noisy data. RidgeCV also addresses other important issues, such as data overfitting and multicollinearity. First, each regression method is independently applied to gene expression data and the output of these methods, in the form of ranked gene lists, is aggregated using a novel gene score-based method by considering the gene rank and model importance. The model importance score is evaluated based on an adjusted coefficient of determination. This method implicitly includes majority voting by averaging each gene score value across all models. The proposed model was tested on the DREAM4 datasets and publicly available small-scale real-world network datasets. Experiments with noisy datasets showed that the proposed ensemble model is more accurate and efficient than other state-of-the-art methods. © 2023 IEEE.

A study into the impact of data breaches of electronic health records

- Pilla, Ravi, Oseni, Taiwo, Stranieri, Andrew

A survey on image classification of lightweight convolutional neural network

- Liu, Ying, Xiao, Peng, Fang, Jie, Zhang, Dengsheng

  • Authors: Liu, Ying , Xiao, Peng , Fang, Jie , Zhang, Dengsheng
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023, Harbin, China, 29-31 July 2023, 2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
  • Full Text: false
  • Reviewed:
  • Description: In recent years, deep neural networks have achieved tremendous success in image classification in both academic and industrial settings. However, the high hardware requirements imposed by their intensive and complex computations pose a challenge for deployment on low-storage devices. To address this challenge, lightweight networks provide a viable solution. This paper provides a detailed review of recent lightweight image classification algorithms, which can be categorized into low-redundancy network model design and neural network compression algorithms. The former reduces network computations by replacing traditional convolution with efficient lightweight convolution, while the latter reduces redundancy in the network by employing methods such as network pruning, knowledge distillation, and parameter quantization. We summarize the experimental results of some classical models and algorithms on ImageNet2012 and CIFAR-10 datasets, and analyze the characteristics, advantages and disadvantages of these models respectively. Finally, future research directions for lightweight algorithms in the field of image classification are identified. © 2023 IEEE.

Artificial intelligence enabled digital twin for predictive maintenance in industrial automation system : a novel framework and case study

- Siddiqui, Mustafa, Appuhamillage, Gayan, Hewawasam, Hasitha

  • Authors: Siddiqui, Mustafa , Appuhamillage, Gayan , Hewawasam, Hasitha
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 IEEE International Conference on Mechatronics, ICM 2023, Leicestershire UK, 15-17 March 2023, Proceedings - 2023 IEEE International Conference on Mechatronics, ICM 2023
  • Full Text: false
  • Reviewed:
  • Description: Industrial automation systems are excessively used in advanced manufacturing environments. These systems are always prone to failure which not only disturbs smooth manufacturing operations but can also cause injuries to operators. Therefore, in this research, a novel predictive maintenance algorithm is proposed that can be used to detect anomalies in the automation system to avoid asset failure. Artificial Intelligence enabled Digital Twin model was used to detect early anomalies to avoid catastrophic effects of equipment failure. Real-time sensor data were used to validate the proposed novel algorithm. The data were recorded via sensors mounted on the physical system. This paper presents the effectiveness of the proposed algorithm to detect anomalies in industrial automation systems under faulty conditions. © 2023 IEEE.

Autoregressive models for biomedical signal processing

- Haderlein, Jonas, Peterson, Andre, Burkitt, Anthony, Mareels, Iven, Grayden, David

Bilateral insider threat detection : harnessing standalone and sequential activities with recurrent neural networks

- Manoharan, Phavithra, Hong, Wei, Yin, Jiao, Zhang, Yanchun, Ye, Wenjie, Ma, Jiangang

Biosynthetic organic solar cell biorefinery to fulfil Australian baseload power demands

- Ghayur, Adeel

  • Authors: Ghayur, Adeel
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023, Wollongong, 3-6 December 2023, 2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023
  • Full Text: false
  • Reviewed:
  • Description: Renewable energy technologies are fundamental to mitigating climate change. However, the intermittent nature associated with wind and solar technologies is the biggest hurdle to their implementation in baseload grid. The other two issues are incorporation of fossil fuel derived materials in their synthesis and end-of-life recycling. These issues for solar panels have been addressed here. In this study, for the first time, a pathway for the incorporation of renewable organic materials in the synthesis of organic solar cells has been developed. While this novel biorefinery concept has been developed for Australia, it is just as applicable in other regions. In this concept, 650,000 metric tons of non-food bio-waste is consumed for the production of organic materials that manufacture solar cells with 14 GW nameplate capacity, annually. In the State of Victoria (Australia) this is sufficient for 2 GW of baseload capacity. In this baseload 12 GW is earmarked for electrolytic hydrogen production to generate 2 GW of fuel cell based power for 18 h, daily, at 50% roundtrip efficiency. The land area required for such a 2 GW baseload solar farm is 200 km2. These results show that less than 300,000 km2 of area (0.2% of Earth's surface) is needed to transition the entire planet's power grid to solar baseload and 150 biorefineries can produce enough organic solar panels to achieve this transition in ten years. At their end-of-life, these solar panels are easier to recycle, when compared to silicon solar panels due to their organic materials. © 2023 IEEE.

Causal deep operator networks for data-driven modeling of dynamical systems

- Nghiem, Truong, Nguyen, Thang, Nguyen, Binh, Nguyen, Linh

  • Authors: Nghiem, Truong , Nguyen, Thang , Nguyen, Binh , Nguyen, Linh
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023, Hybrid, Honolulu, 1-4 October 2023, Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics p. 1136-1141
  • Full Text: false
  • Reviewed:
  • Description: The deep operator network (DeepONet) architecture is a promising approach for learning functional operators, that can represent dynamical systems described by ordinary or partial differential equations. However, it has two major limitations, namely its failures to account for initial conditions and to guarantee the temporal causality - a fundamental property of dynamical systems. This paper proposes a novel causal deep operator network (Causal-DeepONet) architecture for incorporating both the initial condition and the temporal causality into data-driven learning of dynamical systems, overcoming the limitations of the original DeepONet approach. This is achieved by adding an independent root network for the initial condition and independent branch networks conditioned, or switched on/off, by time-shifted step functions or sigmoid functions for expressing the temporal causality. The proposed architecture was evaluated and compared with two baseline deep neural network methods and the original DeepONet method on learning the thermal dynamics of a room in a building using real data. It was shown to not only achieve the best overall prediction accuracy but also enhance substantially the accuracy consistency in multistep predictions, which is crucial for predictive control. © 2023 IEEE.

Clinically prioritized data visualization in remote patient monitoring

- Arora, Teena, Balasubramanian, Venki, Stranieri, Andrew, Neupane, Arun

Comprehensive analysis of feature extraction techniques and runtime performance evaluation for phishing detection

- Nath, Subrata, Islam, Mohammad, Chowdhury, Abdullahi, Rashid, Mohammad, Islam, Maheen, Jabid, Taskeed, Naha, Ranesh

Data-efficient graph learning meets ethical challenges

- Tang, Tao