This paper proposes a human-in-the-loop framework that integrates machine learning models with semantic technologies to aid decision making in the domain of steelmaking. To achieve this, we convert a random forest (RF) into rules in a Semantic Web Rule Language (SWRL) format and represent real-world data as a knowledge graph in a Resource Description Framework (RDF) format, capturing the meta-data as part of the model. A rule engine is deployed that applies logical inference on the knowledge graph, resulting in a semantically enriched classification. This new classification is combined with external domain-expert knowledge to provide improved, knowledge-guided assistance for the human-in-the-loop system. A case study in the steel manufacturing domain is introduced, where this application is used for real-world predictive analytic purposes.

Vehicle detection in parking areas provides the spatial and temporal utilisation of parking spaces. Parking observations are typically performed manually, limiting the temporal resolution due to the high labour cost. This paper uses simulated data and transfer learning to build a robust real-world model for vehicle detection and classification from single-beam LiDAR of a roadside parking scenario. The paper presents a synthetically augmented transfer learning approach for LiDAR-based vehicle detection and the implementation of synthetic LiDAR data. A synthetic augmented transfer learning method was used to supplement the small real-world data set and allow the development of data-handling techniques. In addition, adding the synthetically augmented transfer learning method increases the robustness and overall accuracy of the model. Experiments show that the method can be used for fast deployment of the model for vehicle detection using a LIDAR sensor.

The sudden failure of industrial components can adversely affect an organisation regarding time, cost, and workflow. To prevent downtime that could result in a major disruption, it is crucial to keep equipment components in optimal condition. Hence developing a system that can detect an equipment failure early is essential. The present work proposes a computational strategy of fault diagnosis using Machine Learning (ML) algorithms for an external gear pump. Due to the lack of sufficient experimental data, a novel approach of creating a high-fidelity in-silico data using a Computational Fluid Dynamic (CFD) model of the gear pump in both normal and defective working conditions (viscosity variations, radial gap variations, speed variations, etc.) is introduced. A novel synthetic data generation technique is implemented by perturbing the frequency content of the time series to recreate other working conditions. These synthetically generated datasets are used to train the underlying ML metamodel. Three different ML classification algorithms are used for fault diagnosis, namely Multilayer Perceptron (MLP), Naive Bayes (NB),and Support Vector Machine (SVM).