• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.213-215
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• 2022

Recently, deep learning technology that processes images has been widely used in fire detection, autonomous driving, and defective product detection. In particular, in order to determine whether a product is contaminated or not, it can be identified through the contaminants passed from the existing sensor data, but technologies for recognizing cracks in products or contaminants themselves as images are being actively studied in various fields. In this paper, a system for classifying uncontaminated normal cups and contaminated cups through images was designed and implemented. The image was analyzed using an open image and a photographed image, and the image was analyzed by extracting the upper part of the cup image using Google Objectron for 3D object recognition. Through this study, it is thought that it will be used in various ways for research that can extract the contamination level of products required in the hygiene field based on images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.28-30
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• 2022

Recently, the number of confirmed cases has increased again with the new variant of COVID-19. Quarantine is recommended, especially to prevent the rapidly increasing spread, as environmental controls, such as minimizing contact with others, can increase safety. In addition, there are often cases in which the patient's condition cannot be confirmed from the standpoint of a guardian, such as visitation being prohibited under certain conditions. At this time, the sensor data values of oxygen, carbon dioxide concentrations, temperature and humidity, and alcohol, which are medical gases used in hospitals, are collected remotely using ZigBee wireless communication technology. Design a system that can be stored and monitored in a database. We propose an environmental monitoring system, which is a visualization system designed to allow hospitals to check and feedback data on the managed environment, and to give reliability to parents.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.87-95
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• 2020

Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.441-446
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• 2023

Electrical and optical characteristics of the GaN-based light-emitting diode (LED) with the improved multi-quantum well (MQW) structure have been studied for light source in bio-sensing systems. Novel GaN/In_0.1GaN/In_0.2GaN/In_0.1GaN/GaN and Al_0.1GaN/GaN/In_0.2GaN/GaN/Al_0.1GaN (MQW) structures were suggested, and their radiative recombination rate, light output power, electroluminescence, and external quantum efficiency were compared with those of the conventional GaN/In_0.2GaN/GaN MQW structure using device simulation. The LED with the GaN/In_0.1GaN/In_0.2GaN/In_0.1GaN/GaN MQW structure showed an excellent recombination rate of 5.57 × 10²⁸ cm^-3·s^-1 that was more than one order improvement over that of the conventional LED. In addition, the efficiency droop was relieved by the suggested stepped MQW structure.

• Fashion & Textile Research Journal
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• v.25 no.6
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• pp.673-689
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• 2023

To realize the traceability of sustainable textile products, this study presents a low-carbon process through energy savings in the textile material manufacturing process. Traceability is becoming an important element of Life Cycle Assessment (LCA), which confirms the eco-friendliness of textile products as well as supply chain information. Textile products with complex manufacturing processes require traceability of each step of the process to calculate carbon emissions and power usage. Additionally, an understanding of the characteristics of the product planning-manufacturing-distribution process and an overall understanding of carbon emissions sources are required. Energy use in the textile material manufacturing stage produces the largest amount of carbon dioxide, and the amount of carbon emitted from processes such as dyeing, weaving and knitting can be calculated. Energy saving methods include efficiency improvement and energy recycling, and carbon dioxide emissions can be reduced through waste heat recovery, sensor-based smart systems, and replacement of old facilities. In the dyeing process, which uses a considerable amount of heat energy, LNG, steam can be saved by using "heat exchangers," "condensate management traps," and "tenter exhaust fan controllers." In weaving and knitting processes, which use a considerable amount of electrical energy, about 10- 20% of energy can be saved by using old compressors and motors.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.1-7
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• 2023

The ability of 3D/4D printing technology to create arbitrary 3D structures provides a greater degree of freedom in the design of printed structures. This capability has influenced the field of electronics and biomedical applications by enabling the trends of device miniaturization, customization, and personalization. Here, the current state-of-the-art knowledge of 3D printed electronics and biomedical applications with the unique and unusual properties enabled by 3D/4D printing is reviewed. Specifically, the review encompasses emerging areas involving recyclable and degradable electronics, metamaterial-based pressure sensor, fully printed portable photodetector, biocompatible and high-strength teeth, bioinspired microneedle, and transformable tube array for 3D cell culture and histology.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.837-847
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• 2023

In this study, we explored a method for assessing the extent of damage caused by chemical substances at an accident site through the use of a vegetation index. Data collection involved the deployment of two different drone types, and the damaged area was determined using photogrammetry technology from the 3D point cloud data. To create a vegetation index image, we utilized spectral band data from a multi-spectral sensor to generate an orthoimage. Subsequently, we conducted statistical analyses of the accident site with respect to the damaged area using a predefined threshold value. The Kappa values for the vegetation index, based on the near-infrared band and the green band, were found to be 0.79 and 0.76, respectively. These results suggest that the vegetation index-based approach for analyzing damage areas can be effectively applied in investigations of chemical accidents.

• Tunnel and Underground Space
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• v.33 no.5
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• pp.425-434
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• 2023

To measure the wear amount of the TBM disk cutter in real time, it is important not only to automate the measurement using sensors, but also to stably transmit the measured data to the information processing system. In this study, we investigated the viability of utilizing Bluetooth Low Energy (BLE) technology to wirelessly transmit sensor data from the TBM cutter head to a receiver located at the chamber's rear. Through laboratory experiments, we analyzed the Received Signal Strength Index (RSSI) of the receiver considering various signal strength of the transmitter, separation distances between the transmitter and receiver and chamber fill materials. Our results demonstrate that wireless data transmission is feasible across all tested conditions when the transmitter signal strength is 0 dBm or higher.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1353-1362
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• 2023

Various methods are being attempted to resolve the inconvenience of blood glucose meters used to check blood sugar levels. In this paper, we attempted to estimate blood sugar levels non-invasively using machine learning technology from spectral data acquired using a near-infrared sensor. The non-invasive blood glucose meter used in the study has a total of six near-infrared ray emitters, including visible rays, and a light receiver that receives them. It is a device created to collect spectral data on specific parts of the human body, such as the fingers. To verify whether there was a significant difference depending on blood sugar level, we attempted to estimate blood sugar level through machine learning algorithms. As a result of applying five machine learning algorithm techniques to the collected data and adjusting various hyper parameters, it was confirmed that the support vector regression algorithm showed the best performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.445-452
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• 2024

This paper analyzes the navigation error for each rotational motion in order to design an optimal rotation sequence, which is a key technology in the rotational inertial navigation. Rotational inertial navigation system is designed to cancel out navigation errors caused by inertial sensor errors by periodically rotating the inertial measurement unit. A properly sequenced rotational motion cancels out the maximum amount of navigation error and is known as an optimal rotation sequence. To design such an optimal turning procedure, this paper identifies the feasible rotational motions that can be implemented in a rotational inertial navigation system and analyzes the navigation error introduced by each rotational motion. In addition, by analyzing the characteristics of the navigation error generated during a rotation sequence in combination, this paper presents the conditions for designing an optimal rotation sequence.