• Smart Structures and Systems
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• v.24 no.2
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• pp.243-256
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• 2019

Determination of the most meaningful structural modes and gaining insight into how these modes evolve are important issues for long-term structural health monitoring of the long-span bridges. To address this issue, modal parameters identified throughout the life of the bridge need to be compared and linked with each other, which is the process of mode tracking. The modal frequencies for a long-span bridge are typically closely-spaced, sensitive to the environment (e.g., temperature, wind, traffic, etc.), which makes the automated tracking of modal parameters a difficult process, often requiring human intervention. Machine learning methods are well-suited for uncovering complex underlying relationships between processes and thus have the potential to realize accurate and automated modal tracking. In this study, Gaussian mixture model (GMM), a popular unsupervised machine learning method, is employed to automatically determine and update baseline modal properties from the identified unlabeled modal parameters. On this foundation, a new mode tracking method is proposed for automated mode tracking for long-span bridges. Firstly, a numerical example for a three-degree-of-freedom system is employed to validate the feasibility of using GMM to automatically determine the baseline modal properties. Subsequently, the field monitoring data of a long-span bridge are utilized to illustrate the practical usage of GMM for automated determination of the baseline list. Finally, the continuously monitoring bridge acceleration data during strong typhoon events are employed to validate the reliability of proposed method in tracking the changing modal parameters. Results show that the proposed method can automatically track the modal parameters in disastrous scenarios and provide valuable references for condition assessment of the bridge structure.

• The Journal of Bigdata
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• v.3 no.2
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• pp.101-112
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• 2018

The purpose of the study is to predict the number of police calls using neural network which is one of the machine learning and negative binomial regression, by using the data of 112 police calls received from Chungnam Provincial Police Agency from June 2016 to May 2017. The variables which may affect the police calls have been selected for developing the prediction model : time, holiday, the day before holiday, season, temperature, precipitation, wind speed, jurisdictional area, population, the number of foreigners, single house rate and other house rate. Some variables show positive correlation, and others negative one. The comparison of the methods can be summarized as follows. Neural network has correlation coefficient of 0.7702 between predicted and actual values with RMSE 2.557. Negative binomial regression on the other hand shows correlation coefficient of 0.7158 with RMSE 2.831. Neural network has low interpretability, but an excellent predictability compared with the negative binomial regression. Based on the prediction model, the police agency can do the optimal manpower allocation for given values in the selected variables.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.33 no.12
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• pp.53-63
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• 2017

The purpose of this study is to present an integrated analysis for energy use and its patterns as vertical locations of the dwelling units in apartment buildings which are located in an urban area and constructed by a renowned contractor. In order to enhance the effectiveness of the method, the original data of electricity, water, and gas bills which directly reflect the energy use are sorted and analyzed into several groups as vertical locations in each building. And also, by use of comparing and contrasting the data on a monthly and yearly basis, the accuracy of analyses for seasonal energy use and its patterns is strengthened. Comparative analyses used in this study describe the results that vertical locations of dwelling units do not have much influence on electricity and water usage, but are closely related with gas usage for a heating season. According to the analysis of gas usage, the units on the ground and right above pilotis need enhancement in the insulations for heating to mitigate energy loss. Also, the analysis for the middle floor units in each group describe the fact that the gas usage of the units on the ground is consumes 1.5 times greater than that of the typical floors. Therefore, enhanced insulation strategies need to be considered against the adverse condition that the heat loss increases as the wall facing the outside air increases or as the wind velocity increases through the pilotis.

• The Journal of the Acoustical Society of Korea
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• v.40 no.1
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• pp.1-9
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• 2021

High-frequency bistatic sea surface scattering channels according to sea state were measured at an experimental site of Geoje bay in April 2020, and compared with predictions based on scattering theory. A linear frequency-modulated signal with a center frequency of 128 kHz and a bandwidth of 32 kHz was used for the acoustic measurements. Sea surface wavenumber spectrum was calculated from surface roughness data measured by a wave buoy, and bistatic scattering cross-section of Small Slope Approximation (SSA) based on the wavenumber spectrum was estimated. In addition, scattering from near-surface bubbles using wind speed measured during experiments was considered. Surface scattering channel intensity impulse responses were simulated using the scattering cross-section and the simulation results were compared and analyzed with the field data.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.35-42
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• 2021

This paper presents an on-line finite element model updating method for in-service structures using measured data. Conventional updating methods, which are based on numerical optimization, are not efficient for on-line updating because they generally require repeated eigenvalue analyses until convergence criteria are met. The proposed method enables fully automated on-line finite element model updating, almost simultaneously with vibration measurement, without any user intervention or off-line procedures. The automated covariance-driven stochastic subspace identification (Cov-SSI) method is utilized to identify modal frequencies and vectors, and the identified modal data is fed to the neural network of the inverse eigenvalue function to produce the updated finite element model parameters. Numerical examples for a wind excited 20-story building structure shows that the proposed method can update the series of finite element model parameters automatically. It is also shown that sudden changes in the structural parameters can be detected and traced successfully.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.26 no.1
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• pp.1-10
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• 2021

In this study, Information for the case of seawater flooding and observation data over a period of 10 years (2009~2018) was collected. Using machine learning algorithms, the characteristics of the types of seawater flooding and observations by type were classified. Information for the case of seawater flooding was collected from the reports of the Korea Hydrographic and Oceanographic Agency (KHOA) and the Korea Land and Geospatial Informatics Corporation. Observation data for ocean and meteorological were collected from the KHOA and the Korea Meteorological Agency (KMA). The classification of seawater flooding incidence types is largely categorized into four types, and into 5 development types through combination of 4 types. These types were able to distinguish the types of seawater flooding according to the marine weather environment. The main characteristics of each was classified into the following groups: tidal movement, low pressure system, strong wind, and typhoon. Besides, in consideration of the geographical characteristics of the ocean, the thresholds of ocean factors for seawater flooding by region and type were derived.

• Atmosphere
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• v.31 no.5
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• pp.539-551
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• 2021

In this paper, the weather records in 'Nanjung Ilgi' were investigated and the weather characteristics of the southern coast of Korea (SC_Korea) was discussed. The Nanjung Ilgi is a personal diary written by admiral Yi Sun-sin from January 1592 to November 1598 during the 7-year war caused by the Japanese invasion. He is a respected great leader in the history of world naval warfare, winning all 23 battles against the Japanese. Of the 1593 days of diaries currently preserved, only 42 days have no weather records. Weather was recorded in detail, including sky conditions, precipitation, wind characteristics and others. Weather records were extracted from the diary, converted to the solar calendar, and compared with the meteorological data of Yeosu. The average annual precipitation day is about 90 days, which is similar to the current 95~100 days. As in the current climate, precipitation frequently occurs for about 30 days in summer, but less than 15 days in other seasons, and the rainy season starts from June 14 to 21 and ends from July 6 to 17. It seems that the abnormal cold and heat phenomena, which deviate significantly from the seasonal average climate, occurred on 6 and 21 days, respectively, over 7 years. This means that the weather records of Nanjung Ilgi can be used as valuable data on the climate of SC_Korea in the late 16th century. The fact that he recorded the weather even in such extreme battle conditions shows that he clearly recognized the importance of weather in warfare.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.55-61
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• 2022

In order to reduce greenhouse gases, the main culprit of global warming, the United Nations signed the Climate Change Convention in 1992. Korea is also pursuing a policy to expand the supply of renewable energy to reduce greenhouse gas emissions. The expansion of renewable energy development using solar power led to the expansion of wind power and solar power generation. The expansion of renewable energy development, which is greatly affected by weather conditions, is creating difficulties in managing the supply and demand of the power system. To solve this problem, the power brokerage market was introduced. Therefore, in order to participate in the power brokerage market, it is necessary to predict the amount of power generation. In this paper, the prediction system was used to analyze the Yonchuk solar power plant. As a result of applying solar insolation from on-site (Model 1) and the Korea Meteorological Administration (Model 2), it was confirmed that accuracy of Model 2 was 3% higher. As a result of comparative analysis of the DNN and RNN models, it was confirmed that the prediction accuracy of the DNN model improved by 1.72%.

• Journal of Aerospace System Engineering
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• v.17 no.1
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• pp.79-87
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• 2023

To design a control law of an aircraft, an accurate aircraft dynamic model is required. To obtain an aerodynamic database (DB) to build a dynamic model, a large number of wind tunnel tests are typically required. However, when flight test data of target aircraft exist such as in the process of unmanned conversion of a manned aircraft, an aircraft dynamic model can be obtained through a parameter estimation method and a DB tuning procedure. This paper describes a nonlinear model construction process and a verification method for KC-100 OPV aircraft. Flight data compatibility analysis was performed to determine suitability of the estimation method application. Linear model estimation was performed using the maximum likelihood estimation method. Results of aerodynamic DB tuning process and verification applying the FFS standard to the nonlinear model constructed are presented.

• Smart Media Journal
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• v.12 no.5
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• pp.81-87
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• 2023

In this paper, accidents in high-voltage overhead distribution lines, which provide stable power supply in the power system, cause inconvenience in life and disruption of production of companies. 22.9 [kV] high-voltage overhead power distribution lines aim to improve reliability and stability, such as damage caused by rain, snow, wind, etc., or electric shock prevention. Therefore, in order to prevent wire disconnection accidents due to deterioration of electrical conductivity or tensile strength due to corrosion of overhead distribution lines, it is necessary to prevent unexpected accidents in the future through regular inspection and repair. In order to diagnose deterioration due to corrosion of distribution lines, a diagnostic system (measuring instrument) is installed on the wires to monitor the condition of the wires. The manager on the ground receives the measured data through ZigBee wireless communication, controls the diagnosis system through the diagnosis system program, and grasps the condition of the overhead distribution line through the measured data and photographed photos, and predicts the life of the wire along with the visual inspection method. developed a program.