All Outputs (8)

Machine Learning Pipeline for Energy and Environmental Prediction in Cold Storage Facilities (2024)
Journal Article
Alkhulaifi, N., Bowler, A. L., Pekaslan, D., Serdaroglu, G., Closs, S., Watson, N. J., & Triguero, I. (2024). Machine Learning Pipeline for Energy and Environmental Prediction in Cold Storage Facilities. IEEE Access, 12, 153935-153951. https://doi.org/10.1109/access.2024.3482572

As energy demands and costs rise, enhancing energy efficiency in Food and Drink Cold Storage (FDCS) rooms is important for reducing expenses and achieving environmental sustainability ambitions. Forecasting electricity use in FDCSs can help optimise... Read More about Machine Learning Pipeline for Energy and Environmental Prediction in Cold Storage Facilities.

Interval Agreement Weighted Average - Sensitivity to Data Set Features (2024)
Presentation / Conference Contribution
Zhao, Y., Wagner, C., Ryan, B., Pekaslan, D., & Navarro, J. (2024, June). Interval Agreement Weighted Average - Sensitivity to Data Set Features. Presented at 2024 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Yokohama, Japan

The growing use of intervals in fields like survey analysis necessitates effective aggregation methods that can summarize and represent such uncertain data representations. The Interval Agreement Approach (IAA) addresses this by aggregating interval... Read More about Interval Agreement Weighted Average - Sensitivity to Data Set Features.

Fuzzy Uncertainty-Based Out-of-Distribution Detection Algorithm for Semantic Segmentation (2023)
Presentation / Conference Contribution
Lin, Q., Chen, X., Chen, C., Pekaslan, D., & Garibaldi, J. M. (2023, August). Fuzzy Uncertainty-Based Out-of-Distribution Detection Algorithm for Semantic Segmentation. Presented at 2023 IEEE International Conference on Fuzzy Systems (FUZZ), Songdo Incheon, Korea

Deep learning models have achieved high performance in numerous semantic segmentation tasks. However, when the input data at test time do not resemble the training data, deep learning models can not handle them properly and will probably produce poor... Read More about Fuzzy Uncertainty-Based Out-of-Distribution Detection Algorithm for Semantic Segmentation.

Alpha-cut based compositional representation of fuzzy sets and exploration of associated fuzzy set regression (2022)
Presentation / Conference Contribution
Pekaslan, D., & Wagner, C. (2022, July). Alpha-cut based compositional representation of fuzzy sets and exploration of associated fuzzy set regression. Presented at 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2022), Padova, Italy

The compositional representation of data and associated statistical approaches is a powerful framework for modelling and reasoning about quantities which reflect proportions of a whole. Recently, an increasing body of work has started exploring the a... Read More about Alpha-cut based compositional representation of fuzzy sets and exploration of associated fuzzy set regression.

Predictability of higher heating value of biomass feedstocks via proximate and ultimate analyses – A comprehensive study of artificial neural network applications (2022)
Journal Article
Güleç, F., Pekaslan, D., Williams, O., & Lester, E. (2022). Predictability of higher heating value of biomass feedstocks via proximate and ultimate analyses – A comprehensive study of artificial neural network applications. Fuel, 320, Article 123944. https://doi.org/10.1016/j.fuel.2022.123944

Higher heating value (HHV) is a key characteristic for the assessment and selection of biomass feedstocks as a fuel source. The HHV is usually measured using an adiabatic oxygen bomb calorimeter; however, this method can be time consuming and expensi... Read More about Predictability of higher heating value of biomass feedstocks via proximate and ultimate analyses – A comprehensive study of artificial neural network applications.

An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems (2021)
Presentation / Conference Contribution
Chen, C., Zhao, Y., Wagner, C., Pekaslan, D., & Garibaldi, J. M. (2021, July). An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems. Presented at 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Luxembourg

Recent years have seen a surge in interest in non-singleton fuzzy systems. These systems enable the direct modelling of uncertainty affecting systems' inputs using the fuzzification stage. Moreover, recent work has shown how different composition app... Read More about An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems.

Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels (2019)
Presentation / Conference Contribution
Pekaslan, D., Wagner, C., & Garibaldi, J. M. (2019, June). Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

Most real-world environments are subject to different sources of uncertainty which may vary in magnitude over time. We propose that while Type-1 (T1) Non-Singleton Fuzzy Logic System (NSFLSs) have the potential to tackle uncertainty within the input... Read More about Leveraging IT2 Input Fuzzy Sets in Non-Singleton Fuzzy Logic Systems to Dynamically Adapt to Varying Uncertainty Levels.

Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems (2018)
Presentation / Conference Contribution
Pekaslan, D., Garibaldi, J. M., & Wagner, C. (2018, October). Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems. Presented at 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan

Real-world environments face a wide range of noise (uncertainty) sources and gaining insight into the level of noise is a critical part of many applications. While Non-Singleton Fuzzy Logic Systems (NSFLSs), in particular recently introduced advanced... Read More about Noise Parameter Estimation for Non-Singleton Fuzzy Logic Systems.