Search Results (3)

A comprehensive roadmap for advancing Electric Micromobility (EMM) systems addressing the fragmented and scarce information available in the field is defined as a transformative solution for urban transportation, targeting short-distance trips with compact, lightweight vehicles under 350 kg and maximum speeds of 45 km/h, such as bicycles, e-scooters, and skateboards, which offer flexible, eco-friendly alternatives to traditional transportation, easing congestion and promoting sustainable urban mobility ecosystems. This review aims to guide researchers by consolidating key technical insights and offering a foundation for future exploration in this domain. It examines critical components of EMM systems, including electric motors, batteries, power converters, and control strategies. Likewise, a comparative analysis of electric motors, such as PMSM, BLDC, SRM, and IM, highlights their unique advantages for micromobility applications. Battery technologies, including Lithium Iron Phosphate, Nickel Manganese Cobalt, Nickel-Cadmium, Sodium-Sulfur, Lithium-Ion and Sodium-Ion, are evaluated with a focus on energy density, efficiency, and environmental impact. The study delves deeply into power converters, emphasizing their critical role in optimizing energy flow and improving system performance. Furthermore, control techniques like PID, fuzzy logic, sliding mode, and model predictive control (MPC) are analyzed to enhance safety, efficiency, and adaptability in diverse EMM scenarios by using cutting-edge semiconductor devices like Silicon Carbide (SiC) and Gallium Nitride (GaN) in well-known configurations, such as buck, boost, buck–boost, and bidirectional converters to ensure great efficiency, reduce energy losses, and ensure compact and reliable designs. Ultimately, this review not only addresses existing gaps in the literature but also provides a guide for researchers, outlining future research directions to foster innovation and contribute to the development of sustainable, efficient, and environmentally friendly urban transportation systems. Full article

This article presents a cost-effective camera network system that employs neural network-based object detection and stereo vision to assist a pan–tilt–zoom camera in imaging fast, erratically moving small aerial targets. Compared to traditional radar systems, this approach offers advantages in supporting real-time target differentiation and ease of deployment. Based on the principle of knowledge distillation, a novel data augmentation method is proposed to coordinate the latest open-source pre-trained large models in semantic segmentation, text generation, and image generation tasks to train a BicycleGAN for image enhancement. The resulting dataset is tested on various model structures and backbone sizes of two mainstream object detection frameworks, Ultralytics’ YOLO and MMDetection. Additionally, the algorithm implements and compares two popular object trackers, Bot-SORT and ByteTrack. The experimental proof-of-concept deploys the YOLOv8n model, which achieves an average precision of 82.2% and an inference time of 0.6 ms. Alternatively, the YOLO11x model maximises average precision at 86.7% while maintaining an inference time of 9.3 ms without bottlenecking subsequent processes. Stereo vision achieves accuracy within a median error of 90 mm following a drone flying over 1 m/s in an 8 m $\times$ 4 m area of interest. Stable single-object tracking with the PTZ camera is successful at 15 fps with an accuracy of 92.58%. Full article

In interior space planning, the furnishing stage usually entails manual iterative processes, including meeting design objectives, incorporating professional input, and optimizing design performance. Machine learning has the potential to automate and improve interior design processes while maintaining creativity and quality. The aim of this study was to develop a furnishing method that leverages machine learning as a means for enhancing design processes. A secondary aim was to develop a set of evaluation metrics for assessing the quality of the results generated from such methods, enabling comparisons between the performance of different models. To achieve these aims, floor plans were tagged and assembled into a comprehensive dataset that was then employed for training and evaluating three conditional generative adversarial network models (pix2pix, BicycleGAN, and SPADE) to generate furniture layouts within given room boundaries. Post-processing methods for improving the generated results were also developed. Finally, evaluation criteria that combine measures of architectural design with standard computer vision parameters were devised. Visual architectural analyses of the results confirm that the generated rooms adhere to accepted architectural standards. The numerical results indicate that BicycleGAN outperformed the two other models. Moreover, the overall results demonstrate a machine-learning workflow that can be used to augment existing interior design processes. Full article