• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.57-68
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• 2018

A system to prevent disasters by collecting and analyzing environmental information such as road surface sedimentation, sinkholes, collapse risk of bridges, temperature and humidity around tram station is continuously monitored by monitoring the condition of road surface when constructing tram which is one of the urban railways. In this paper, we propose a wireless network security management system for tram roads based on LPWA that can recognize risk factors of road surface, bridge and tram station of tram in advance and prevent risk. The proposed system consists of a sensor node that detects the state of the tram road surface, a gateway that collects sensor information, and a safety management system that monitors the safety and environmental conditions of the tram road surface, and applies the low power long distance communication technology. As a result of comparing the proposed system with the LTE system in the field test, it was confirmed that there is no significant difference between the sensor information value and the critical alarm level in the monitoring system.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.183-189
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• 2021

In recent years, active research has been devoted toward developing a monitoring system using ambient vibration data in order to quantitatively determine the deterioration occurring in a structure over a long period of time. This study developed a low-cost edge computing system that detects the abnormalities in structures by utilizing the dynamic characteristics acquired from the structure over the long term for ensemble learning. The system hardware consists of the Raspberry Pi, an accelerometer, an inclinometer, a GPS RTK module, and a LoRa communication module. The structural abnormality detection afforded by the ensemble learning using dynamic characteristics is verified using a laboratory-scale structure model vibration experiment. A real-time distributed processing algorithm with dynamic feature extraction based on the experiment is installed on the Raspberry Pi. Based on the stable operation of installed systems at the Community Service Center, Pohang-si, Korea, the validity of the developed system was verified on-site.

• Journal of Platform Technology
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• v.9 no.4
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• pp.79-89
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• 2021

In this paper, the fine dust (PM10) and ultrafine dust (PM2.5) measurement platforms are designed to be mobile and fixed using oneM2M, the international standard for IoT. The fine dust measurement platform is composed and designed with a fine dust measurement device, agent, oneM2M platform, oneM2M IPE, and monitoring system. The main difference between mobile and fixed is that the mobile uses the MQTT protocol for interconnection between devices and services without blind spots based on LTE connection, and the fixed uses the LoRaWAN protocol with low power and wide communication range. Not only fine dust, but also temperature, humidity, atmospheric pressure, volatile organic compounds (VOC), carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and noise data related to daily life were collected. The collected sensor values were managed using the common API provided by oneM2M through the agent and oneM2M IPE, and it was designed into four resource types: AE and container. Six functions of operability, flexibility, convenience, safety, reusability, and scalability were analyzed through the fine dust measurement platform design.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2158-2165
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• 2023

Marine radioactivity monitoring is critical for taking immediate action in case of unexpected nuclear accidents at nuclear facilities located near coastal areas. Especially when the level of contamination is not predictable, mobile monitoring systems will be useful for wide-area ocean radiation survey and for determination of the level of radioactivity. Here, we used a silicon photomultiplier and a high-efficiency GAGG crystal to fabricate a compact, battery-powered gamma spectroscopy that can be used in an ocean environment. The developed spectroscopy has compact dimensions of 6.5 × 6.5× 8 cm³ and weighs 560 g. We used LoRa, a low-power wireless protocol for communication. Successful data transmission was achieved within 1.4 m water depth. The developed gamma spectroscopy was able to detect radioactivity from a ¹³⁷Cs point source (3.7 kBq) at a distance of 20 cm in water. Moreover, we demonstrated an unmanned radioactivity monitoring system in a real sea by combining unmanned surface vehicle with the developed gamma spectroscopy. A hidden ¹³⁷Cs source (3.07 MBq) was detected by the unmanned system at a distance of 3 m. After successfully testing the developed mobile spectroscopy in an ocean environment, we believe that our proposed system will be an effective solution for mobile real-time marine radioactivity monitoring.

• ETRI Journal
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• v.45 no.4
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• pp.570-580
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• 2023

Sensor nodes are the most significant part of a wireless sensor network that offers a powerful combination of sensing, processing, and communication. One major challenge while designing a sensor node is power consumption, as sensor nodes are generally battery-operated. In this study, we proposed the design of a low-power, long range-based wireless sensor node with flexibility, a compact size, and energy efficiency. Furthermore, we improved power performance by adopting an efficient hardware design and proper component selection. The Nano Power Timer Integrated Circuit is used for power management, as it consumes nanoamps of current, resulting in improved battery life. The proposed design achieves an off-time current of 38.17309 nA, which is tiny compared with the design discussed in the existing literature. Battery life is estimated for spreading factors (SFs), ranging from SF7 to SF12. The achieved battery life is 2.54 years for SF12 and 3.94 years for SF7. We present the analysis of current consumption and battery life. Sensor data, received signal strength indicator, and signal-to-noise ratio are visualized using the ThingSpeak network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.682-685
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• 2021

The design and fabrication of LTE Cat.M1 (3GPP Release13 Standardization) modules of ships such as fishing boat and patrol boat are reported in this research. LTE cat.M1 modules are needed to expand the for broadening of IoT services through the ships used in fishing gear automatically identification monitoring system, which is one of applying ICT into the real name system of electric fishing gear.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.899-901
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• 2022

본 연구는 '지능형 스마트 안전모 개발'에 관한 것으로, IoT 기술을 기반으로 공사현장에서 발생하는 각종 사고를 방지하기 위한 스마트 안전모를 개발하고, 기존 안전모에 존재하는 한계를 극복하는 것을 목표로 한다. 이를 위해 IoT 기술인 비콘, 블루투스를 활용하여 작업자 주변 환경을 실시간으로 판단하고 작업자를 주변 위험요소로부터 보호한다. 각종 생체센서를 활용하여 작업자의 안전모 착용여부와 건강상태에 대해서 실시간으로 송수신한다. 또한 LoRaWAN의 P2P LR Communication을 이용하여 공사현장의 특성인 통신음영지대를 제거하여 서비스를 안정적으로 제공할 수 있다.

• Smart Structures and Systems
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• v.31 no.3
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• pp.301-309
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• 2023

Tilt is a key indicator of structural safety. Real-time monitoring of tilt responses helps to evaluate structural condition, enable cost-effective maintenance, and enhance lifetime resilience. This paper presents a prototype wireless sensing system for structural tilt measurement. Long range (LoRa) technology is adopted by the sensing system to offer long-range wireless communication with low power consumption. The sensor integrates a gyroscope and an accelerometer as the sensing module. Although tilt can be estimated from the gyroscope or the accelerometer measurements, these estimates suffer from either drift issue or high noise. To address this challenging issue and obtain more reliable tilt results, two sensor fusion algorithms, the complementary filter and the Kalman filter, are investigated to fully exploit the advantages of both gyroscope and accelerometer measurements. Numerical simulation is carried out to validate and compare the sensor fusion algorithms. Laboratory experiment is conducted on a simply supported beam under moving vehicle load to further investigate the performance of the proposed wireless tilt sensing system.

• Journal of KIBIM
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• v.14 no.2
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• pp.13-24
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• 2024

This research suggests a novel visualization approach utilizing Generative AI to render photorealistic architectural alternatives images in the early design phase. Photorealistic rendering intuitively describes alternatives and facilitates clear communication between stakeholders. Nevertheless, the conventional rendering process, utilizing 3D modelling and rendering engines, demands sophisticate model and processing time. In this context, the paper suggests a rendering approach employing the text-to-image method aimed at generating a broader range of intuitive and relevant reference images. Additionally, it employs an Text-to-Image method focused on producing a diverse array of alternatives reflecting architects' styles when visualizing the exteriors of residential buildings from the mass model images. To achieve this, fine-tuning for architects' styles was conducted using the Low-Rank Adaptation (LoRA) method. This approach, supported by fine-tuned models, allows not only single style-applied alternatives, but also the fusion of two or more styles to generate new alternatives. Using the proposed approach, we generated more than 15,000 meaningful images, with each image taking only about 5 seconds to produce. This demonstrates that the Generative AI-based visualization approach significantly reduces the labour and time required in conventional visualization processes, holding significant potential for transforming abstract ideas into tangible images, even in the early stages of design.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.141-143
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• 2022

This paper presented a new technology to identify traffic violations of motorbike riders by detecting the traffic signal using You Only Look Once (YOLO) object detection. The hardware module that is mounted on the front of the motorbike consists of Raspberry Pi with a camera to run the YOLO object detection, a GPS module to acquire the motorcycle's coordinate, and a LoRa communication module to send the data to a cloud DB. The main goal of the software is to determine whether a motorbike has violated a traffic signal. This paper proposes a function to recognize the red traffic signal colour with its movement inside the camera angle and determine that the traffic signal violation happens if the traffic signal is moving to the right direction (the rider turns left) or moving to the top direction (the riders goes straight). Furthermore, if a motorbike rider is violated the signal, the rider's personal information (name, mobile phone number, etc), the snapshot of the violation situation, rider's location, and date/time will be sent to a cloud DB. The violation information will be delivered to the driver's smartphone as a push notification and the local police station to be used for issuing violation tickets, which is expected to prevent motorbike riders from violating traffic signals.