Dynamic Identification for Representative Building Typologies: Three Case Studies from Bucharest Area

The paper presents results from an experimental program implemented for three representative buildings in Bucharest metropolitan area and aimed to explore the potential of various dynamic identification methods in providing information about building state changes. The objective is to establish reference values of potential use in rapid earthquake damage detection systems. Each of the selected buildings was designed according to a different seismic code, in force at the time of its construction. The methods employed for this study were: the analysis of Fourier spectra, the analysis of the transfer function and the random decrement technique. To validate the results, the fundamental periods of vibration determined experimentally were compared with the corresponding values predicted by the empirical formulas specified in the corresponding editions of the Romanian seismic code. The results revealed consistent values for both the fundamental period and the damping ratio of the buildings. However, small variations of the two parameters were identified, depending on the time the recordings were performed, noise sources and levels and building occupancy. The results, in terms of validated data on the dynamic characteristics of Romanian building stock and of assessment of methods performance, add up to the information pool needed for the development of countrywide pre- and post-earthquake assisted decision tools

Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges

Radar is a key component of the suite of perception sensors used for safe and reliable navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity imaging, detection of agents in occlusion and over long ranges, and robust performance in adverse weather conditions. However, the usage of radar data presents some challenges: it is characterized by low resolution, sparsity, clutter, high uncertainty, and lack of good datasets. These challenges have limited radar deep learning research. As a result, current radar models are often influenced by lidar and vision models, which are focused on optical features that are relatively weak in radar data, thus resulting in under-utilization of radar's capabilities and diminishing its contribution to autonomous perception. This review seeks to encourage further deep learning research on autonomous radar data by 1) identifying key research themes, and 2) offering a comprehensive overview of current opportunities and challenges in the field. Topics covered include early and late fusion, occupancy flow estimation, uncertainty modeling, and multipath detection. The paper also discusses radar fundamentals and data representation, presents a curated list of recent radar datasets, and reviews state-of-the-art lidar and vision models relevant for radar research. For a summary of the paper and more results, visit the website: autonomous-radars.github.io

蛍光および化学発光を利用した生体分子の定量および機能性評価手法の確立

蛍光および化学発光を利用し、動植物における重要な生体分子の定量および機能性評価手法の確立を目指した。北九州市立大