• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.2
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• pp.134-144
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• 2007

In this study, a smart sensor unit is developed by using the smart sensor technology that is being rapidly developed in recent years for structural health monitoring system, and its performance is evaluated through various experiments, and also, damage detection experiment is performed on a model structure. This paper as the first half of this study contains the development and performance evaluation of the smart sensor. In the latter half of this study, structure damage detection experiment is performed for the application of verified smart sensor unit into structural health monitoring, and it is compared with a wire measurement system. The smart sensor is developed by using high-power wireless modem, MEMS Sensor and AVR microcontroller, and an embedded program is also developed for the control and operation of the sensor unit. To verify the performance of the smart sensor, many experiments are performed for sensitivity and resolution analysis tests, data acquisition by using cantilever beam and shaker, and on-site application using actual bridge. As a result, the smart sensor proves to be satisfactory in its performance.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.667-669
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• 2004

본 논문에서는 경제성, 효율성, 확장성 및 개발의 편의성을 고려한 환자 모니터링 모바일 시스템의 개발 사례를 보고한다. 특히 생명징후(Vital Sign) 중에서도 체온(Body Temperature)을 모니터링 하는 모바일 시스템 MBTMS 개발을 다루었다. MBTMS(Mobile Body Temperature Monitoring System)는 다음과 같이 세 부분으로 구성된다: (1) 환자의 체온들 수집하기 위한 센서, (2) 센서를 통해 체온을 측정하는 모듈, (3) 모듈에서 전달받은 자료를 처리 분석, 저장하는 PC(Personal Computer) Application으로 구성된다. 이러한 MBTMS는 입원환자 및 노약자를 위한 경제적인 PMS(Patient Monitoring System) 개발 및 e-Health 연구의 한 부분으로 활용될 수 있을 것이다.

• Structural Monitoring and Maintenance
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• v.7 no.2
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• pp.109-124
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• 2020

Benefiting from the massive monitoring data collected by the Structural health monitoring (SHM) system, scholars can grasp the complex environmental effects and structural state during structure operation. However, the monitoring data is often missing due to sensor faults and other reasons. It is necessary to study the recovery method of missing monitoring data. Taking the structural temperature monitoring data of Nanjing Dashengguan Yangtze River Bridge as an example, the long short-term memory (LSTM) network-based recovery method for missing structural temperature data is proposed in this paper. Firstly, the prediction results of temperature data using LSTM network, support vector machine (SVM), and wavelet neural network (WNN) are compared to verify the accuracy advantage of LSTM network in predicting time series data (such as structural temperature). Secondly, the application of LSTM network in the recovery of missing structural temperature data is discussed in detail. The results show that: the LSTM network can effectively recover the missing structural temperature data; incorporating more intact sensor data as input will further improve the recovery effect of missing data; selecting the sensor data which has a higher correlation coefficient with the data we want to recover as the input can achieve higher accuracy.

• Smart Structures and Systems
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• v.1 no.2
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• pp.159-184
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• 2005

The paper intends to summarize some guidelines for future smart structure system application in military aircraft. This preview of system integration is based upon a review on approximately one and a half decades of application oriented aerospace related smart structures research. Achievements in the area of structural health monitoring, adaptive shape, adaptive load bearing devices and active vibration control have been reached, potentials have been identified, several feasibility studies have been performed and some smart technologies have been already implemented. However the realization of anticipated visions and previously initial timescales announced have been rather too optimistic. The current development shall be based on a more realistic basis including more emphasis on fundamental aircraft strength, stiffness, static and dynamic load and stability requirements of aircraft and interdisciplinary integration requirements and improvements of integrated actors, actuator systems and control systems including micro controllers.

• Smart Structures and Systems
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• v.30 no.6
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• pp.649-659
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• 2022

Structural Health Monitoring (SHM) researchers have identified Augmented Reality (AR) as a new technology that can assist inspections. Post-seismic structural inspections are conducted to evaluate the safety level of the damaged structures. Quantification of nearby structural changes over short-term and long-term periods can provide building inspectors with information to improve their safety. This paper proposes a Time Machine Measure (TMM) application based on an Augmented Reality (AR) Head-Mounted-Device (HMD) platform. The primary function of TMM is to restore the saved meshes of a past environment and overlay them onto the real environment so that inspectors can intuitively measure dynamic structural deformation and other environmental movements. The proposed TMM application was verified by demo experiments simulating a real inspection environment.

• International Journal of High-Rise Buildings
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• v.11 no.3
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• pp.221-226
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• 2022

Installation of Structural Health Monitoring (SHM) system is a legal obligation for high-rise buildings over 200 m or 50-floor high in South Korea. CNP Dongyang has developed key technologies for SHM system design, installation, and data analyzing. Also, CNP Dongyang has applied SHM technology to a plenty of South Korea's representative high-rise buildings. The SHM technology, also, could be used in safety management of construction phase, evaluation of structural performance, etc. In this paper, state of the art SHM technologies and their application examples are introduced to give insight for future research and practical use of SHM.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.439-459
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• 2010

Structural health monitoring (SHM) of civil infrastructure using wireless smart sensor networks (WSSNs) has received significant public attention in recent years. The benefits of WSSNs are that they are low-cost, easy to install, and provide effective data management via on-board computation. This paper reports on the deployment and evaluation of a state-of-the-art WSSN on the new Jindo Bridge, a cable-stayed bridge in South Korea with a 344-m main span and two 70-m side spans. The central components of the WSSN deployment are the Imote2 smart sensor platforms, a custom-designed multimetric sensor boards, base stations, and software provided by the Illinois Structural Health Monitoring Project (ISHMP) Services Toolsuite. In total, 70 sensor nodes and two base stations have been deployed to monitor the bridge using an autonomous SHM application with excessive wind and vibration triggering the system to initiate monitoring. Additionally, the performance of the system is evaluated in terms of hardware durability, software stability, power consumption and energy harvesting capabilities. The Jindo Bridge SHM system constitutes the largest deployment of wireless smart sensors for civil infrastructure monitoring to date. This deployment demonstrates the strong potential of WSSNs for monitoring of large scale civil infrastructure.

• Annual Conference of KIPS
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• 2011.04a
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• pp.89-91
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• 2011

Diabetes is a chronic disease which may lead to other life threatening health complications like heart disease, stroke and peripheral vascular disease that diminished quality of life. In order to assists diabetic patients, we develop a smartphone application to monitor the regular medicine intake and exercise routines through repetitive user friendly and motivational reminders. Health-practitioners including doctors and nurses can frequently view and analyze the daily routine of patients through a web-interface. This paper presents architecture to improve the quality of life with android smart phone as user terminal, and integration of webbased application through web services for health care professionals. We also explain how each of the application interacts with each other and internal integration.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.1
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• pp.78-88
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• 2022

Objectives: To develop the smart sensor to protect worker's health from chemical exposure by adopting ICT (Information and Communications Technology) technologies. Methods: To develope real-time chemical exposure monitoring system, IoT (Internet of Things) sensor technology and regulations were reviewed. We developed and produced smart sensor. A smart sensor is a system consisting of a sensor unit, a communication unit, and a platform. To verify the performance of smart sensors, each sensor has been certified by the Korea Laboratory Accreditation Scheme (KOLAS). Results: Chemicals (TVOC; Total Volatile Organic Compounds, Cl₂: Chlorine, HF: Hydrogen fluoride and HCN: Hydrogen cyanide) were selected according to a priority logic (KOSHA Alert, acute poisoning statistics, literature review). Notifications were set according to OEL (occupational exposure limit). Sensors were selected based on OEL and the capabilities of the sensors. Communication is designed to use LTE (Long Term Evolution) and Wi-Fi at the same time for convenience. Electronic platform were applied to build this monitoring system. Conclusions: Real-time monitoring system for OEL of hazardous chemicals in workplace was developed. Smart sensor can detect chemicals to complement monitoring of traditional workplace environmental monitoring such as short term and peak exposure. Further research is needed to expand the scope of application, improve reliability, and systematically application.