Improved Discriminative Object Localization Algorithm for Safety Management of Indoor Construction.

Object localization is a sub-field of computer vision-based object recognition technology that identifies object classes and locations. Studies on safety management are still in their infancy, particularly those aimed at lowering occupational fatalities and accidents at indoor construction sites. In comparison to manual procedures, this study suggests an improved discriminative object localization (IDOL) algorithm to aid safety managers with visualization to improve indoor construction site safety management.

Broadband metasurface superstrate for polarization-independent wave focusing and gain enhancement at Ka-band.

A broadband metasurface flat lens is proposed as a polarization-independent wideband superstrate for wave focusing and gain enhancement at Ka-band. The proposed metasurface structure consists of four metal layers and is designed with diagonally symmetric unit cells to accommodate both the vertical and horizontal polarizations. The focusing ability of the proposed metasurface flat lens is validated via simulation and measurement, where normally incident plane waves are shown to be enhanced by up to 11 dB as a result of wave focusing.

Damage-Detection Approach for Bridges with Multi-Vehicle Loads Using Convolutional Autoencoder.

Deep learning has been widely employed in recent studies on bridge-damage detection to improve the performance of damage-detection methods. Unsupervised deep learning can be effectively utilized to increase the applicability of damage-detection approaches. Hence, the authors propose a convolutional-autoencoder (CAE)-based damage-detection approach, which is an unsupervised deep-learning network.

Single-layer phase gradient mmWave metasurface for incident angle independent focusing.

Metasurfaces allow the rapid development of compact and flat electromagnetic devices owing to their capability in manipulating the wavefront of electromagnetic waves. Particularly, with respect to the metasurface lenses, wide operational bandwidth and wide incident angle behavior are critically required for practical applications. Herein, a single-layer phase gradient metasurface lens is presented to achieve millimeter-wave focusing at a focal point of 13 mm regardless of the incident angle.

A Novelty Detection Approach for Tendons of Prestressed Concrete Bridges Based on a Convolutional Autoencoder and Acceleration Data.

The most important structural element of prestressed concrete (PSC) bridges is the prestressed tendon, and in order to ensure safety of such bridges, it is very important to determine whether the tendon is damaged. However, it is not easy to detect tendon damage in real time. This study proposes a novelty detection approach for damage to the tendons of PSC bridges based on a convolutional autoencoder (CAE).