Deep Learning-Based Real-Time Auto Classification of Smartphone Measured Bridge Vibration Data.

In this study, a simple and customizable convolution neural network framework was used to train a vibration classification model that can be integrated into the measurement application in order to realize accurate and real-time bridge vibration status on mobile platforms. The inputs for the network model are basically the multichannel time-series signals acquired from the built-in accelerometer sensor of smartphones, while the outputs are the predefined vibration categories. To verify the effectiveness of the proposed framework, data collected from long-term monitoring of bridge were used for training a model, and its classification performance was evaluated on the test set constituting the data collected from the same bridge but not used previously for training.

Development of Tactile Imaging for Underwater Structural Damage Detection.

Underwater structural damage inspection has mainly relied on diver-based visual inspection, and emerging technologies include the use of remotely operated vehicles (ROVs) for improved efficiency. With the goal of performing an autonomous and robotic underwater inspection, a novel Tactile Imaging System for Underwater Inspection (TISUE) is designed, prototyped, and tested in this paper. The system has two major components, including the imaging subsystem and the manipulation subsystem.

The Korean guideline for cervical cancer screening.

The incidence rate of cervical cancer in Korea is still higher than in other developed countries, notwithstanding the national mass-screening program. Furthermore, a new method has been introduced in cervical cancer screening. Therefore, the committee for cervical cancer screening in Korea updated the recommendation statement established in 2002.