Automation

The increasing digitalization of electrical substations, enabled by IEC 61850-based architectures, has improved operational efficiency while expanding the cyber attack surface. This paper introduces a standards-aligned cybersecurity risk mitigation model specifically designed for digital substations and mapped to representative attack scenarios. The model integrates preventive, detective, and application-level controls derived from NIST SP 800-82r3, IEC 62443, and ISO/IEC 27019, and is validated in a laboratory process-bus environment. A baseline risk assessment identified four high-risk scenarios in the studied digital substation architecture. For validation, a selected subset of controls was experimentally evaluated against two representative attack vectors, namely false data injection (FDI) on GOOSE messages and denial-of-service (DoS) against PTP synchronization. For the remaining scenarios, the post-mitigation effects were reassessed analytically based on control coverage, architectural exposure, and standards-aligned cybersecurity reasoning. The experimental validation demonstrated that both empirically tested high-risk scenarios (FDI on GOOSE and DoS on PTP) were effectively mitigated, reducing their residual risk to moderate and low levels, respectively. For the remaining two scenarios, a post-mitigation analytical reassessment based on control coverage and architectural exposure suggested a consistent risk reduction trend, although without direct experimental confirmation. Under this combined empirical–analytical assessment, the number of high-risk scenarios decreased from four to one, corresponding to a 50% experimentally validated reduction in high-risk exposure, complemented by an analytical reassessment of the remaining scenarios. These results provide quantitative evidence about the effectiveness of the model, even with partial implementation. The scientific contribution of this study lies in integrating multistandard cybersecurity requirements into an operational mitigation model tailored to IEC 61850 substations, combined with experimental risk quantification in a realistic process-bus testbed. The proposed model offers practical guidance for utilities and establishes a scalable foundation for advancing cybersecurity in critical power infrastructure.

Accurate detection and severity estimation of corrosion on metallic surfaces is essential for maintaining material integrity and ensuring operational safety in industrial systems. To address limitations in manual inspection methods, this study presents a two-stage deep learning pipeline tailored for high-resolution scanning electron microscopy images. The framework combines instance-level corrosion segmentation using the YOLOv8-seg architecture with subsequent severity classification performed by EfficientNet-B0 and ResNet18. In the segmentation stage, models are trained using both manually annotated and automatically generated binary masks, enabling robust instance mask prediction through prototype-based mask decoding. The classification stage assesses the severity of corrosion by analyzing localized regions based on morphological features, leveraging convolutional neural networks optimized for binary output. The experimental results demonstrate strong performance: the segmentation model trained on manual annotations achieves a Mean Intersection over Union (mIoU) of 89.91, a mask mAP@50 of 98.6, and an ROC-AUC of 94.69. For severity classification, EfficientNet-B0 achieves an accuracy of 93.75% and an F1-score of 93.29, outperforming ResNet18. The proposed framework connects advanced SEM with state-of-the-art machine learning. It provides a scalable, annotation-efficient way to use intelligent and automated corrosion characterization in materials science and industrial applications.

This paper presents a lightweight MATLAB-based framework with a graphical interface for modeling, 3D simulation, trajectory generation, and experimental validation of a 6-DOF industrial robot. The platform integrates kinematic modeling using the rigidBodyTree structure, animated visualization, and both predefined and user-defined trajectory planning within a unified environment. A central aspect of the proposed approach is the implementation of a ROS-compatible TCP/IP communication protocol that avoids the need for a full ROS core installation while preserving compatibility with ROS-Industrial standards. This enables bidirectional data exchange between MATLAB and the robot controller within a simplified architecture. Communication performance tests indicate round-trip latency in the tens-of-milliseconds range and consistent StateServer update rates, supporting monitoring, trajectory execution, and digital twin synchronization in non-real-time conditions. Experiments conducted on an ABB IRB120 robot demonstrate a close correspondence between simulated and real motion, with RMSE below 0.0075 rad and MAE below 0.0065 rad across all joints. All data are stored in JSON format to support reproducibility and further analysis. By integrating simulation and real robot execution within a modular architecture, the proposed framework provides a practical tool for education, rapid prototyping, and experimental research in industrial robotics, while offering a basis for future extensions toward advanced control strategies and digital twin applications.