• Smart Structures and Systems
• /
• v.6 no.4
• /
• pp.405-421
• /
• 2010

This paper proposes a method for early warning of hazard for pipelines. Many pipelines transport dangerous contents so that any damage incurred might lead to catastrophic consequences. However, most of these damages are usually a result of surrounding third-party activities, mainly the constructions. In order to prevent accidents and disasters, detection of potential hazards from third-party activities is indispensable. This paper focuses on recognizing the running of construction machines because they indicate the activity of the constructions. Acoustic information is applied for the recognition and a novel pipeline monitoring approach is proposed. Principal Component Analysis (PCA) is applied. The obtained Eigenvalues are regarded as the special signature and thus used for building feature vectors. One-class Support Vector Machine (SVM) is used for the classifier. The denoising ability of PCA can make it robust to noise interference, while the powerful classifying ability of SVM can provide good recognition results. Some related issues such as standardization are also studied and discussed. On-site experiments are conducted and results prove the effectiveness of the proposed early warning method. Thus the possible hazards can be prevented and the integrity of pipelines can be ensured.

• Journal of Climate Change Research
• /
• v.6 no.1
• /
• pp.55-60
• /
• 2015

Agro-Meteorological Information Service (AMIS) network has been established since 2001 by Rural Development Administration (RDA) in Korea, and has provided access to current and historical weather data with useful information for agricultural activities. AMIS network includes 158 automated weather stations located mostly in farm region, with planning to increase by 200 stations until 2017. Agrometeorological information is disseminated via the web site (http://weather.rda.go.kr) to growers, researchers, and extension service officials. Our services will give enhanced information from observation data (temperature, precipitation, etc.) to application information, such as drought index, agro-climatic map, and early warning service. AMIS network of RDA will help the implementation of an early warning service for weather risk management.

• Tribology and Lubricants
• /
• v.36 no.6
• /
• pp.297-306
• /
• 2020

This paper reviews studies on the types and functions of oil sensors used for machine condition monitoring. Machine condition monitoring is essential for maintaining the reliability of machines and can help avoid catastrophic failures while ensuring the safety and longevity of operation. Machine condition monitoring involves several components, such as compliance monitoring, structural monitoring, thermography, non-destructive testing, and noise and vibration monitoring. Real-time monitoring with oil analysis is also utilized in various industries, such as manufacturing, aerospace, and power plants. The three main methods of oil analysis are off-line, in-line, and on-line techniques. The on-line method is the most popular among these three because it reduces human error during oil sampling, prevents incipient machine failure, reduces the total maintenance cost, and does not need complicated setup or skilled analysts. This method has two advantages over the other two monitoring methods. First, fault conditions can be noticed at the early stages via detection of wear particles using wear particle sensors; therefore, it provides early warning in the failure process. Second, it is convenient and effective for diagnosing data regardless of the measurement time. Real-time condition monitoring with oil analysis uses various oil sensors to diagnose the machine and oil statuses; further, integrated oil sensors can be used to measure several properties simultaneously.

• Journal of the Korean Geophysical Society
• /
• v.8 no.1
• /
• pp.7-14
• /
• 2005

Early detection in real-time response of slope movements ensures tremendous saving of lives and repair costs from catastrophic disaster. Therefore, it is essential to constantly monitor the performance and integrity of slope-stabilizing structures such as Rock bolt, Nail and Pile during or after installation. We developed a novel monitoring system using Fiber Bragg Grating (FBG) sensor. It's advantages are highly sensitivity, small dimension and electro-magnetic immunity. capability of multiplexing, system integrity, remote sensing - these serve real-time health monitoring of the structures. Real-time strain measurement by the signal processing program is shown graphically and it gives a warning sound when the monitored strain state exceeds a given threshold level so that any sign of abnormal disturbance on the spot can be easily perceived.

• Journal of the Korean Geosynthetics Society
• /
• v.17 no.4
• /
• pp.11-19
• /
• 2018

Although many researches related to monitoring and automatic measuring devices for early warning system during slope failure have been carried out in Korea and aboard, most of the researches have installed measuring devices on the slope surface, and there are only few researches about warning systems that can detect and warn before slope failure and disaster occurs. In this study, slope failure simulation experiment was performed by attaching sensors to rock bolts, and initial slope behavior characteristics during slope failure were analyzed. Also, the experiment results were compared and reviewed with varied slope conditions, i.e. shotcrete slope and natural slope, and varied material conditions, i.e. GFRP and steel rock bolt. This study can be used as a basic data in development of warning and alarm system for early evacuation through early detection and warning before slope failure occurs in steep slopes and slope failure vulnerable areas.

• Journal of Korean Society for Quality Management
• /
• v.45 no.1
• /
• pp.25-38
• /
• 2017

Purpose: Hot strip rolling mill consists of a lot of mechanical and electrical units. In condition monitoring and diagnosis phase, various units could be failed with unknown reasons. In this study, we propose an effective method to detect early the units with abnormal status to minimize system downtime. Methods: The early warning problem with various units is defined. K-means and PAM algorithm with Euclidean and Manhattan distances were performed to detect the abnormal status. In addition, an performance of the proposed algorithm is investigated by field data analysis. Results: PAM with Manhattan distance(PAM_ManD) showed better results than K-means algorithm with Euclidean distance(K-means_ED). In addition, we could know from multivariate field data analysis that the system reliability of hot strip rolling mill can be increased by detecting early abnormal status. Conclusion: In this paper, clustering-based monitoring and fault detection algorithm using Manhattan distance is proposed. Experiments are performed to study the benefit of the PAM with Manhattan distance against the K-means with Euclidean distance.

• Journal of Biomedical Engineering Research
• /
• v.42 no.4
• /
• pp.175-185
• /
• 2021

Purpose: Vital sign are used to help assess the general physical health of a person, give clues to possible diseases, and show progress toward recovery. Researchers are using vital sign data and AI(artificial intelligence) to manage a variety of diseases and predict mortality. In order to analyze vital sign data using AI, it is important to select and extract vital sign data suitable for research purposes. Methods: We developed a method to visualize vital sign and early warning scores by processing retrospective vital sign data collected from EMR(electronic medical records) and patient monitoring devices. The vital sign data used for development were obtained using the open EMR big data MIMIC-III and the wearable patient monitoring device(CareTaker). Data processing and visualization were developed using Python. We used the development results with machine learning to process the prediction of mortality in ICU patients. Results: We calculated NEWS(National Early Warning Score) to understand the patient's condition. Vital sign data with different measurement times and frequencies were sampled at equal time intervals, and missing data were interpolated to reconstruct data. The normal and abnormal states of vital sign were visualized as color-coded graphs. Mortality prediction result with processed data and machine learning was AUC of 0.892. Conclusion: This visualization method will help researchers to easily understand a patient's vital sign status over time and extract the necessary data.

• Journal of the Korean Geosynthetics Society
• /
• v.18 no.4
• /
• pp.107-114
• /
• 2019

In this paper, the P-wave multiple detection system for the fast and accurate earthquake early warning nearby the epicenter was developed. The developed systems were installed in five selected public buildings for the validation. During the monitoring, a magnitude 2.3 earthquake occurred in Pohang on 26 September 2019. P-wave initial detection algorithms were operated in three out of four systems installed in Pohang area and recorded as seismic events. At the nearest station, 5.5 km from the epicenter, P-wave signal was detected 1.2 seconds after the earthquake, and S-wave was reached 1.02 seconds after the P-wave reached, providing some alarm time. The maximum accelerations recorded in three different stations were 6.28 gal, 6.1 gal, and 5.3 gal, respectively. The alarm algorithm did not work, due to the high threshold of the maximum ground acceleration (25.1 gal) to operate it. If continuous monitoring and analysis are to be carried out in the future, the developed system could use a highly effective earthquake warning system suitable for the domestic situation.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.6 no.2
• /
• pp.29-38
• /
• 2010

It often occur to nature disaster that like earthquake, typhoon, etc. around KOREA. A Haiti and Chile also metropolitan area of KOREA occur earthquake. in result, People think of nature disaster. Structures of present age are easily affected by nature disaster. So we are important that warn of dangerous situation as soon as possible. On this study, I introduce Integrated monitoring system that administrator check a event as early. I develop Monitoring System using SMS(Short Message Service). Administrator always monitor structure on real-time using mobile web-page. As Administrator using mobile device like PDA, Administrator always monitor structure. As using this system, Damage of nature disaster is minimized and is prevented post damage.

• Tribology and Lubricants
• /
• v.37 no.6
• /
• pp.203-207
• /
• 2021

This study presents the development of dielectric constant sensors to measure lubricant properties. The lubricant oil sensor is used to measure oil properties and machine conditions. Various condition monitoring methods are applied to diagnose machine conditions. Machine condition monitoring using oil sensors has advantage over other machine condition monitoring methods. The fault conditions can be noticed at the early stages by the detection of wear particles using oil sensors. Therefore, it provides an early warning in the failure procedure. A variety of oil sensors are applied to check the machine condition. Among all oil sensors, only one sensor can measure the tendency of several properties such as acidity and water content. A dielectric constant sensor is also used to measure various oil properties; therefore, it is very useful. The dielectric constant is the ratio of the capacitance of a capacitor using that material as a dielectric to that of a similar capacitor using vacuum as its dielectric. The dielectric constant has an effect on water content, contaminants, base oil, additive, and so forth. In this study, the dielectric constant sensor is fabricated using MEMS process. In the fabrication process, the shape, gap of the electrode array, and thickness of the insulation material are considered to improve the sensitivity of the sensor.