Underground utility tunnels (UUTs) are convenient for the integrated management of various infrastructure facilities. They ensure effective control of underground facilities and reduce occupied space. However, aging UUTs require effective management and preventive measures for fire safety. The fundamental problems in operating UUTs are the frequent occurrence of mold, corrosion, and damage caused to finishing materials owing to inadequate waterproofing, dehumidification, and ventilation facilities, which result in corrosion-related electrical leakage in wiring and cables. To prevent this, an abnormal sound detection technology is developed in this study based on acoustic sensing. An acoustic sensor is used to detect electric sparks in the moldy environments of UUTs using a system to collect and analyze the sound generated in the UUTs. We targeted the sound that had the highest impact on detecting electric sparks and performed U-Net-based noise reduction and two-dimensional convolutional neural network-based abnormal sound detection. A mock experiment was conducted to verify the performance of the proposed model. The results indicated that local and spatial features could capture the internal characteristics of both abnormal and normal sounds. The superior performance of the proposed model verified that the local and spatial features of electric sparks are crucial for detecting abnormal sounds.

Recently, VR-based training applications have become popular and promising, as they can simulate real-world situations in a safe, repeatable, and cost-effective way. For immersive simulations, various input devices have been designed and proposed to increase the effectiveness of training. In this study, we developed a novel device that generates 3D hand motion data and provides haptic force feedback for VR interactions. The proposed device can track 3D hand positions using a combination of the global position estimation of ultrasonic sensors and the hand pose estimation of inertial sensors in real time. For haptic feedback, shape–memory alloy (SMA) actuators were designed to provide kinesthetic forces and an efficient power control without an overheat problem. Our device improves upon the shortcomings of existing commercial devices in tracking and haptic capabilities such that it can track global 3D positions and estimate hand poses in a VR space without using an external suit or tracker. For better flexibility in handling and feeling physical objects compared to exoskeleton-based devices, we introduced an SMA-based actuator to control haptic forces. Overall, our device was designed and implemented as a lighter and less bulky glove which provides comparable accuracy and performance in generating 3D hand motion data for a VR training application (i.e., the use of a fire extinguisher), as demonstrated in the experimental results.