• Journal of the Institute of Electronics and Information Engineers
• /
• v.49 no.9
• /
• pp.322-327
• /
• 2012

Parallel-plate-type scanning sensors have been commercially used for X-ray imaging sensors. In this study, we manufactured the scan typed 1D X-ray image sensor that can be used to obtain scanning images, by using the plasma display panel (PDP) fabrication process. We fabricated drift and pixel electrodes in the glass chamber and injected Xe gas at atmospheric pressure. We evaluated the intensity of a pixel signal depending on the bias voltage on the drift electrode and investigated the characteristics of shielding effect on the single pixel using lead (Pb). The adsorption rate of X-ray photon is low (4%) on the soda lime glass (1.1mm) and the electrical signal detected on the X-ray sensor was increased in the high bias voltage. We acquired digital X-ray scanning image with our DAS (data acquisition system) and sensor scanning system.

• Journal of the Institute of Convergence Signal Processing
• /
• v.24 no.2
• /
• pp.82-89
• /
• 2023

Due to the characteristics of ship structure, the compartment structure is complicated and narrow, so safety accidents frequently occur during the work process. The main causes of accidents include structural collisions, falling objects, toxic substance leaks, fires, explosions, asphyxiation, and more. Understanding the on-site conditions of workers during accidents is crucial for mitigating damages. In order to ensure safety, the on-site situation is monitored using CCTV in the ship, but it is difficult to prevent accidents with the existing method. To address this issue, a smart safety helmet equipped with location identification and voice/video communication capabilities is being developed as a safety technology. Additionally, the smart safety helmet incorporates environmental sensors for temperature, humidity, vibration, noise, tilt (gyro sensor), and gas detection within the work area. These sensors can notify workers wearing the smart safety helmet of hazardous situations. By utilizing the smart safety helmet and environmental sensors, the safety of workers aboard ships can be enhanced.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.54 no.4
• /
• pp.50-58
• /
• 2017

Recently, some methods of converging heterogeneous fire sensor data have been proposed for effective fire detection, but the rule-based methods have low adaptability and accuracy, and the fuzzy inference methods suffer from detection speed and accuracy by lack of consideration for images. In addition, a few image-based deep learning methods were researched, but it was too difficult to rapidly recognize the fire event in absence of cameras or out of scope of a camera in practical situations. In this paper, we propose a novel fire detection system combining a deep learning algorithm based on CNN and fuzzy inference engine based on heterogeneous fire sensor data including temperature, humidity, gas, and smoke density. we show it is possible for the proposed system to rapidly detect fire by utilizing images and to decide fire in a reliable way by utilizing multi-sensor data. Also, we apply distributed computing architecture to fire detection algorithm in order to avoid concentration of computing power on a server and to enhance scalability as a result. Finally, we prove the performance of the system through two experiments by means of NIST's fire dynamics simulator in both cases of an explosively spreading fire and a gradually growing fire.