• Structural Engineering and Mechanics
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• v.43 no.3
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• pp.287-309
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• 2012

Precast segmental bridge columns (PSBC) are alternatives for monolithic cast-in-situ concrete columns in bridge substructures, with fast construction speed and structural durability. The analytical tool for common use is demonstrated applicable for seismic performance prediction of PSBCs through experiment conducted earlier. Then the analytical program was used for parameter optimization of PSBC configurations under reversal cyclic loading. Shear strength by pushover analysis was compared with theoretical prediction. Moreover, seismic response of PSBC with energy dissipation (ED) bars was compared with its no ED bar counterpart under three history ground acceleration records. The investigation shows that appropriate ED bar and post-tensioned tendon arrangement is important for higher lateral bearing capacity and good ductility performance of PSBCs.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.267-272
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• 2014

In this paper, we analyze the characteristics for the driving shock resistance of the military vehicle through the bump test. Prior to the experiment, theoretical analysis was performed by using the SRS(shock response spectrum) and VRS(vibration response spectrum) analysis method. And we estimated the characteristics for the driving shock resistance of the military vehicle. Bump test was performed using the acceleration sensor and the driving test at a different speed. We evaluated the characteristics for the driving shock resistance of the military vehicle based on the result. And predicted values were compared with the theoretical analysis. In addition, we evaluated the results of the theoretical prediction of the SRS and the VRS analysis. And we evaluate the suitability of the prediction method at military vehicle shock analysis.

• Journal of Auto-vehicle Safety Association
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• v.11 no.4
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• pp.22-27
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• 2019

In this paper, the vibration load and analysis results for automotive battery supporter were performed to provide efficient vibration tolerance performance prediction methods for single-product vibration tolerance testing, and the major influencing factors and considerations for setting up single-unit vibration tolerance tests were reviewed. A common applicable standard load was applied to efficiently predict the performance of single-unit vibrations through the frequency response analysis technique. The results similar to test results can be predicted by checking vulnerable parts of the vehicle components for vibration loads and applying scale factor to standard loads. In addition, it was confirmed that the test conditions with a frequency generating the same durability severity as the endurance test are needed for accurate prediction of the durability of the single-unit vibration tolerance test conditions, and the acceleration and frequency with the conditions that there is no significant nonlinear phenomena in the vibration system are established during the single-unit vibration tolerance test conditions.

• Proceedings of the KSR Conference
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• 2006.11b
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• pp.104-111
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• 2006

The non-linear properties of rubber material which are described as strain energy function are important parameter to design and evaluate of rubber spring. These are determined by material tests which are uni-axial tension and bi-axial tension. The computer simulation using the nonlinear element analysis program executed to predict and evaluate the load capacity and stiffness for chevron spring. In order to investigate the heat-aging effects on the rubber material properties, the acceleration test were carried out. Compression set results changes as the threshold are used for assessment of the useful life and time to threshold value were plotted against reciprocal of absolute temperature to give the Arrhenius plot. By using the compression set test, several useful life prediction for rubber material were proposed.

• Proceedings of the KIEE Conference
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• 2005.07c
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• pp.1945-1947
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• 2005

This study considers find out best accelerated life testing and lifetime prediction of electrolytic capacitors. We proved about relation between failure and deterioration mechanism from last thesis. Beside we performed test that temperature and voltage press higher than allowance specification. Failure distribution acquired from those test. And wiebull function and Minitab program applied to accelerated constant and lifetime by means of calculation. At the result, goodness of fit affect to weibull function and acceleration factor therefore fitting is important factor in reliability testing.

• Journal of Sensor Science and Technology
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• v.2 no.1
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• pp.57-64
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• 1993

In this paper the normalized innovations squared of a Kalman filter is used to detect a bias level in prediction errors due to target accelerations. The probability density function of the normalized innovation squared is obtained for a steady state Kalman filter, and it is used to calculate the detection probability of the bias level. A typical example is given to compute the detection probability and to plot the maneuver detector operating characteristic curves.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.11a
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• pp.217-220
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• 2008

Slag mass deposition was required to predict accurate performance of kick motor (KM) system. Slag mass accumulation was analyzed through the aluminum oxide particle paths to predict slag mass deposition. Numerical analysis to solve both flow field and droplet accumulation was performed with Fluent 6.3 program. The effects for the acceleration and diameters of the aluminum oxide particles was analyzed, finally total slag mass accumulation was acquired. It confirmed that the slag mass deposition was agreed well with previously slag mass prediction based on KM ground test.

• Journal of the Earthquake Engineering Society of Korea
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• v.27 no.2
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• pp.111-118
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• 2023

The 2017 Pohang earthquake afflicted more significant economic losses than the 2016 Gyeongju earthquake, even if these earthquakes had a similar moment magnitude. This phenomenon could be due to local site conditions that amplify ground motions. Local site effects could be estimated from methods using the horizontal-to-vertical spectral ratio, standard spectral ratio, and the generalized inversion technique. Since the generalized inversion method could estimate the site effect effectively, this study modeled the site effects in the Korean peninsula using the generalized inversion technique and the Fourier amplitude spectrum of ground motions. To validate the method, the site effects estimated for seismic stations were tested using recorded ground motions, and a ground motion prediction equation was developed without considering site effects.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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• pp.91-99
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• 2024

In this paper, machine learning models were applied to predict the seismic response of steel frame structures. Both geometric and material nonlinearities were considered in the structural analysis, and nonlinear inelastic dynamic analysis was performed. The ground acceleration response of the El Centro earthquake was applied to obtain the displacement of the top floor, which was used as the dataset for the machine learning methods. Learning was performed using two methods: Decision Tree and Random Forest, and their efficiency was demonstrated through application to 2-story and 6-story 3-D steel frame structure examples.

• Journal of Information Technology Applications and Management
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• v.29 no.5
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• pp.27-37
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• 2022

This study intends to link agricultural machine history data with related organizations or collect them through IoT sensors, receive input from agricultural machine users and managers, and analyze them through AI algorithms. Through this, the goal is to track and manage the history data throughout all stages of production, purchase, operation, and disposal of agricultural machinery. First, LSTM (Long Short-Term Memory) is used to estimate oil consumption and recommend maintenance from historical data of agricultural machines such as tractors and combines, and C-LSTM (Convolution Long Short-Term Memory) is used to diagnose and determine failures. Memory) to build a deep learning algorithm. Second, in order to collect historical data of agricultural machinery, IoT sensors including GPS module, gyro sensor, acceleration sensor, and temperature and humidity sensor are attached to agricultural machinery to automatically collect data. Third, event-type data such as agricultural machine production, purchase, and disposal are automatically collected from related organizations to design an interface that can integrate the entire life cycle history data and collect data through this.