The design and performance analysis of relay lenses that provide high-performance image transmission for target acquisition and tracking in military optical systems. Relay lenses are critical components for clear and lossless image transmission over long distances. In this study, the optical performance of a relay lens system designed and optimized using ZEMAX software is investigated in detail. The analysis focuses on important optical properties such as modulation transfer function (MTF), spot diagrams, Seidel diagram, field curvature and distortion. The results show that the lens has significant potential in military applications for target detection and tracking with high resolution and low aberration.

In this article; The step-by-step design of a 3-phase salient synchronous generator and efficiency optimization with particle swarm optimization technique are investigated. Unlike other studies in the literature, a system has been studied to minimize mathematical equation errors by using specific loading parameters that simplify the design process. It has been tried to obtain a program structure that minimizes programming problems by minimizing the number of variables used in the design. With the optimization of the specific loading parameters, the effects on the machine volume, cost and efficiency were examined.

The rising need for high efficiency and precise control has intensified research on wound rotor synchronous motors (WRSMs). Accurate torque estimation is crucial for performance optimization, energy efficiency, and reliable operation. However, traditional physics-based models often fail to capture nonlinearities such as magnetic saturation, geometric dependencies, and parameter variations, leading to errors under real conditions. To address these challenges, this study proposes a data-driven hybrid modeling approach combining physical motor parameters with machine learning algorithms. The dataset was generated through finite element analysis (FEA) simulations of a 6 kW WRSM producing 40 Nm nominal torque. It includes around 2000 geometric and electromagnetic scenarios, each subjected to thorough preprocessing. Three regression models—support vector regression (SVR), random forest, and extreme gradient boosting (XGBoost)—were used for torque prediction. Instead of standard cross-validation, a “Hold-Out Geometry Test” involving an unseen motor geometry evaluated each model’s generalization capability and robustness against overfitting. Results show that the SVR model achieved the best performance with a coefficient of determination R2 = 0.9950, mean absolute error 0.071 Nm, and mean absolute percentage error 0.178%. Shapley Additive Explanations–based interpretability analysis confirmed that the model’s decision process aligns with physical motor behavior. The proposed approach offers high accuracy, interpretability, and strong generalization for predicting torque in WRSMs, even for unseen geometries, while greatly reducing computation time compared to traditional FEA-based analyses. Overall, this study establishes a robust foundation for real-time performance prediction and intelligent control of WRSMs. Future work will extend the model to dynamic operating conditions and incorporate multiphysical parameters such as temperature and material saturation.

Electric vehicles (EVs) have increasingly become central to modern transportation systems due to their advantages in environmental sustainability and energy efficiency. In this development process, electric drive motors play a decisive role in key criteria such as performance, range, energy consumption, and production costs. In this study, three primary motor types commonly used in electric vehicles the Wound Rotor Synchronous Motor (WRSM), Permanent Magnet Synchronous Motor (PMSM), and Induction Motor (IM) are evaluated in a multidimensional manner in terms of their technical performance, energy efficiency, and application segments. Comparative analyses reveal that PMSM motors, in particular, provide an optimal power-to-weight ratio for mid-range vehicles due to their high torque generation and low energy consumption. The compact structure and high efficiency levels of these motors have made them the standard choice for many manufacturers today. On the other hand, WRSM motors offer a strong alternative for long-term sustainability goals by eliminating dependence on rare-earth elements and providing field weakening capability. The high-temperature tolerance, low maintenance requirements, and wide speed range operating capability of WRSM motors indicate their potential for broader adoption in the future. Segment-based evaluations have determined that vehicles in the economic class offer longer range per unit of energy due to lower weight and limited power requirements, representing a significant advantage for compact electric vehicles intended for urban use in terms of energy savings. Conversely, luxury and high-performance segment vehicles demonstrate higher energy consumption despite larger battery capacities; in these segments, factors such as acceleration, traction power, and driving comfort are prioritized over efficiency. The general findings indicate that motor power plays a critical role not only in performance but also in energy consumption, range capacity, and segment-based product positioning. Furthermore, the increasing research and development efforts on alternative motor architectures such as WRSM suggest that these motors may become more attractive both economically and environmentally in the future. In this context, it is recommended that interdisciplinary research on motor technologies be intensified in order to achieve sustainable electric transportation objectives.

Power transformers are among the most critical components of electric power transmission and distribution systems, and unexpected failures can lead to substantial economic losses and prolonged power outages. Dissolved Gas Analysis (DGA) is the most widely used diagnostic technique for transformer fault diagnosis and involves interpreting gases dissolved in insulating oil. However, conventional interpretation methods, such as the Rogers ratio, the Doernenburg ratio, the IEC 60599 ratio method, and the Duval Triangle, rely heavily on expert knowledge, may produce inconsistent diagnoses for the same oil sample, and may fail to provide a diagnosis in certain cases. In this study, 12 classification models were evaluated using the publicly available Power Transformers Fault Detection and Diagnosis (FDD) and Remaining Useful Life (RUL) dataset and a unified evaluation protocol. Model performance was assessed using Accuracy, Balanced Accuracy, and Macro-F1 score, while model interpretability was investigated through Shapley Additive Explanations (SHAP) analysis. The results showed that ensemble tree-based methods achieved the best overall performance. LightGBM and Random Forest both attained an Accuracy of 0.969 and a Macro-F1 score of 0.924, while LightGBM further achieved the highest Balanced Accuracy of 0.940. XGBoost exhibited the most stable performance under cross-validation. SHAP analysis revealed that engineered relative concentration features, particularly the CO/H₂ ratio and the combined gas ratio, were among the most influential features for fault classification. These findings demonstrate that, for datasets of this scale, ensemble tree-based models combined with well-designed features provide strong and interpretable performance for imbalanced DGA-based fault diagnosis, highlighting the effectiveness of feature-based ensemble learning for small- to medium-sized datasets.