• Journal of the Korea institute for structural maintenance and inspection
• /
• v.16 no.1
• /
• pp.99-109
• /
• 2012

This study aimed to develop a Structural Health Monitoring System for steel beams in the manner of suggesting and verifying a theoretical formula for displacement estimation using strain gauges, and estimating the loading points and magnitude. According to the results of this study, it was found that when a load of 160kN (56% of the yield load) was applied, the error rate of the deflection obtained with a strain gauge at the point of maximum deflection compared to the deflection measured with a displacement meter was within 2%, and that the estimates of the magnitude and points of load application also showed the error rate of not more than 1%. This suggests that the displacement and load of steel beams can be measured with strain gauges and further, it will enable more cost-effective sensor designing without displacement meter or load cell. The Structural Health Monitoring System program implemented in Lab VIEW gave graded warnings whenever the measured data exceeds the specified range (strength limit state, serviceability limit state, yield strain), and both the serviceability limit state and strength limit state could be simultaneously monitored with strain gauge alone.

• The Journal of Engineering Geology
• /
• v.24 no.2
• /
• pp.191-199
• /
• 2014

We describe a method for detecting slope instability in flume tests using pore pressure and water content data in conjunction with a statistical control chart analysis. Specifically, we conducted univariate statistical analysis on x-MR control chart data (pore pressure and water content) collected at several points along the flume slope, which we separated into three parts: upper, middle, and lower. To assess our results in the context of landslide forecasting and warning systems, we applied control limit lines at $1{\sigma}$, $2{\sigma}$, and $3{\sigma}$ levels of uncertainty. In doing so, we observed that dispersion time varies depending on the control limit line used. Moreover, the detection of instabilities is highly dependent on the position and type of sensor. Our findings indicate that different characteristics of the data on various factors predict slope failure differently and these characteristics can be identified by univariate statistical analysis. Therefore, we suggest that a univariate statistical approach is an effective method for the early detection of slope instability.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.12 no.4
• /
• pp.525-532
• /
• 2017

The system is consisted of arduino and sensors for protecting bicycle and safety of driver. The speed indicator is composed of speed alarms which are less than 15km/h, 15~30km/h, over 30km/h through limit switch. At that time, the accuracy is 96.6% compared to actual speed. Also, It gives a person warning ablut the obstacle of 5cm tall through ultrasonic sensor in night. Auto Lock System is operated to protect bicycle and the text message is sent to the user, if the bicycle lock was broken. This system puts emphasis on safety and usability, providing a application to know consuming calories.

• Journal of the Korean Society of Safety
• /
• v.31 no.6
• /
• pp.58-66
• /
• 2016

This study aims to estimate the failure probability of concrete gravity dams for their risk analysis under flood situation. To the end, failure modes of concrete gravity dams and their limit state functions are proposed based on numerous review of domestic and international literatures on the dam failure cases and design standards. Three failure modes are proposed: overturning, sliding, and overstress. Based on the failure modes the limit state functions, the failure probability is assessed for a weir section and a non-weir section of a dam in Korea. As water level is rising from operational condition to extreme flood condition, the failure probability is found to be raised up to the warning condition, especially for overturning mode at the non-weir section. The result can be used to reduce the risk of the dam by random environmental variables under possible flood situation.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.95-95
• /
• 2020

Urban flooding occurs in the form of internal-water inundation on roads and lowlands due to heavy rainfall. Unlike in the case of rivers, inundation in urban areas there is lacking in research on predicting and warning through measurement data. In order to analyze urban flood patterns and prevent damage, it is necessary to analyze flooding measurement data for various rainfalls. In this study, the pattern of urban flooding caused by rainfall was analyzed by utilizing the urban flooding measuring sensor, which is being test-run in the flood prone zone for urban flooding management. For analysis, 2019 rainfall data, surface water depth data, and water level data of a street inlet (storm water pipeline) were used. The analysis showed that the amount of rainfall that causes flooding in the target area was identified, and the timing of inundation varies depending on the rainfall pattern. The results of the analysis can be used as verification data for the urban inundation limit rainfall under development. In addition, by using rainfall intensity and rainfall patterns that affect the flooding, it can be used as data for establishing rainfall criteria of urban flooding and predicting that may occur in the future.

• Journal of Korea Water Resources Association
• /
• v.54 no.12
• /
• pp.1285-1294
• /
• 2021

In recent years, as rainfall is concentrated and rainfall intensity increases worldwide due to climate change, the scale of flood damage is increasing. Rainfall of a previously unobserved magnitude falls, and the rainy season lasts for a long time on record. In particular, these damages are concentrated in ASEAN countries, and at least 20 million people among ASEAN countries are affected by frequent flooding due to recent sea level rise, typhoons and torrential rain. Korea supports the domestic flood warning system to ASEAN countries through various ODA projects, but the communication network is unstable, so there is a limit to the central control method alone. Therefore, in this study, an artificial intelligence-based flood prediction model was developed to develop an observation station that can observe water level and rainfall, and even predict and warn floods at once at one observation station. Training, validation and testing were carried out for 0.5, 1, 2, 3, and 6 hours of lead time using the rainfall and water level observation data in 10-minute units from 2009 to 2020 at Junjukbi-bridge station of Seolma stream. LSTM was applied to artificial intelligence algorithm. As a result of the study, it showed excellent results in model fit and error for all lead time. In the case of a short arrival time due to a small watershed and a large watershed slope such as Seolma stream, a lead time of 1 hour will show very good prediction results. In addition, it is expected that a longer lead time is possible depending on the size and slope of the watershed.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.25 no.1
• /
• pp.101-119
• /
• 2022

Existing algal bloom monitoring is based on field sampling, and there is a limit to understanding the spatial distribution of algal blooms, such as the occurrence and spread of algae, due to local investigations. In this study, algal bloom monitoring was performed using an unmanned aerial vehicle and multispectral sensor, and data on the distribution of algae were provided. For the algal bloom monitoring site, data were acquired from the Mulgeum·Mae-ri site located in the lower part of the Nakdong River, which is the areas with frequent algal bloom. The Chlorophyll-a(Chl-a) value of field-collected samples and the Chl-a estimation formula derived from the correlation between the spectral indices were comparatively analyzed. As a result, among the spectral indices, Maximum Chlorophyll Index (MCI) showed the highest statistical significance(R²=0.91, RMSE=8.1mg/m³). As a result of mapping the distribution of algae by applying MCI to the image of August 05, 2021 with the highest Chl-a concentration, the river area was 1.7km², the Warning area among the indicators of the algal bloom warning system was 1.03km²(60.56%) and the Algal Bloom area occupied 0.67km²(39.43%). In addition, as a result of calculating the number of occurrence days in the area corresponding to the "Warning" in the images during the study period (July 01, 2021~November 01, 2021), the Chl-a concentration above the "Warning" level was observed in the entire river section from 12 to 19 times. The algal bloom monitoring method proposed in this study can supplement the limitations of the existing algal bloom warning system and can be used to provide information on a point-by-point basis as well as information on a spatial range of the algal bloom warning area.

• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.4 no.2
• /
• pp.198-207
• /
• 1994

In order to evaluate the effectiveness of environmental intervention of work place, metal mercury concentration in air and in urine of the total 43 workers for 3years from December 1991 to October 1993 in a fluorescent lamp manufacturing industry exposed to mercury, was measured before and after implementation of controls such as establishing exhaust ventilation at the department of exhaustion, coating the floor of work place with epostane, cleaning of the floor, improved housekeeping, and etc. The results were as follows. 1. Before the intervention(December 1991) 39.0% exceeded metal mercury Threshold Limit Value(TLV, $0.05mg/m^3$). After the intervention(October 1993) 10.0% exceeded TLV and geometric mean of mercury in air was $0.1mg/m^3$, and showed effectiveness rate of intervention to be 74.4% 2. After the intervention, geometric means of mercury concentrations in air were 0.013, $0.019mg/m^3$ and showed effectiveness rate of intervention to be 76.6%, 65.5% in A factory(right tube lamp)and at exhaustion department, respectively, A follow up survey fround statistically significant reductions in mercury concetration in air three years later. 3. Mercury concentration in urine of 11 workers(29.7%) exceeded warning level of $100{\mu}l/l$ before the intervention. After the intervention, of 3workers(8.8%) exceeded warning level and geometric mean of mercury concentration($26.5{\mu}l/l$) in urine was 2.4 times than that of before the intervention. Geometric means of mercury concentrations in urine of workers at exhaustion department, at sealing and aging department were 44.0, $77.7{\mu}l/l$, respectively and they decreased 2.3, 3.2 times than that of before the intervention.

• International Journal of Highway Engineering
• /
• v.14 no.1
• /
• pp.103-109
• /
• 2012

Digital speedometer which is supposed to provide the basic data for analyzing human factors of drivers has a limitation for human behavior studies of drivers, because it records limited driving information including GPS velocities. Besides, Black Box, which is currently being actively commercialized in the market, records mostly vehicles' risky patterns rather than drivers' behaviors. As a result, it also shows a limit to analyze dangerous driving patterns. This study performed a risky driving study for human factor analysis. This study conducted before and after comparisons for real time warning study using a risky driving judgment device. The analysis was conducted based on Longitudinal acceleration, Lateral acceleration, and Yaw rate of vehicles.

• Journal of Korean Society of Water and Wastewater
• /
• v.34 no.1
• /
• pp.35-43
• /
• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.