• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.1
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• pp.55-63
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• 2019

In engineering problems, many random variables have correlation, and the correlation of input random variables has a great influence on reliability analysis results of the mechanical systems. However, correlated variables are often treated as independent variables or modeled by specific parametric joint distributions due to difficulty in modeling joint distributions. Especially, when there are insufficient correlated data, it becomes more difficult to correctly model the joint distribution. In this study, multivariate kernel density estimation with bounded data is proposed to estimate various types of joint distributions with highly nonlinearity. Since it combines given data with bounded data, which are generated from confidence intervals of uniform distribution parameters for given data, it is less sensitive to data quality and number of data. Thus, it yields conservative statistical modeling and reliability analysis results, and its performance is verified through statistical simulation and engineering examples.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.12
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• pp.975-985
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• 2018

The drones have been developed for military purposes and are now used in many fields such as logistics, communications, agriculture, disaster, defense and media. As the range of use of drones increases, cases of abuse of drones are increasing. It is necessary to develop anti-drone technology to detect the position of unwanted drones using the physical phenomena that occur when the drones fly. In this paper, we estimate the DOA(direction of arrival) of the drone by using the acoustic signal generated when the drone is flying. In addition, the dynamics model of the drones was applied to the unscented kalman filter to improve the microphone array detection performance and reduce the error of the position estimation. Through simulation, the drone detection performance was predicted and verified through experiments.

• Journal of Environmental Science International
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• v.27 no.12
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• pp.1169-1178
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• 2018

Two man-made carbon emissions, fossil fuel emissions and land use emissions, have been perturbing naturally occurring global carbon cycle. These emitted carbons will eventually be deposited into the atmosphere, the terrestrial biosphere, the soil, and the ocean. In this study, Simple Global Carbon Model (SGCM) was used to simulate global carbon cycle and to estimate global carbon budget. For the model input, fossil fuel emissions and land use emissions were taken from the literature. Unlike fossil fuel use, land use emissions were highly uncertain. Therefore land use emission inputs were adjusted within an uncertainty range suggested in the literature. Simulated atmospheric $CO_2$ concentrations were well fitted to observations with a standard error of 0.06 ppm. Moreover, simulated carbon budgets in the ocean and terrestrial biosphere were shown to be reasonable compared to the literature values, which have considerable uncertainties. Simulation results show that with increasing fossil fuel emissions, the ratios of carbon partitioning to the atmosphere and the terrestrial biosphere have increased from 42% and 24% in the year 1958 to 50% and 30% in the year 2016 respectively, while that to the ocean has decreased from 34% in the year 1958 to 20% in the year 2016. This finding indicates that if the current emission trend continues, the atmospheric carbon partitioning ratio might be continuously increasing and thereby the atmospheric $CO_2$ concentrations might be increasing much faster. Among the total emissions of 399 gigatons of carbon (GtC) from fossil fuel use and land use during the simulation period (between 1960 and 2016), 189 GtC were reallocated to the atmosphere (47%), 107 GtC to the terrestrial biosphere (27%), and 103GtC to the ocean (26%). The net terrestrial biospheric carbon accumulation (terrestrial biospheric allocations minus land use emissions) showed positive 46 GtC. In other words, the terrestrial biosphere has been accumulating carbon, although land use emission has been depleting carbon in the terrestrial biosphere.

• Tunnel and Underground Space
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• v.28 no.6
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• pp.651-669
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• 2018

This study suggests the prediction model to estimate the specific energy of a pick cutter using a gene expression programming (GEP) and particle swarm optimization (PSO). Estimating the performance of mechanical excavators is of crucial importance in early design stage of tunnelling projects, and the specific energy (SE) based approach serves as a standard performance prediction procedure that is applicable to all excavation machines. The purpose of this research, is to investigate the relationship between UCS and BTS, penetration depth, cut spacing, and SE. A total of 46 full-scale linear cutting test results using pick cutters and different values of depth of cut and cut spacing on various rock types was collected from the previous study for the analysis. The Mean Squared Error (MSE) associated with the conventional Multiple Linear Regression (MLR) method is more than two times larger than the MSE generated by GEP-PSO algorithm. The $R^2$ value associated with the GEP-PSO algorithm, is about 0.13 higher than the $R^2$ associated with MLR.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.185-190
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• 2019

This study aims to apply the mathematical method of fractional order derivatives to the controller that controls the system response. In general, the Laplace transform of the PID controller has an exponent of the integer order of s. The derivative of the fractional order has a fractional exponent of s when it is transformed by Laplace transform. Therefore, this controller proposes a design method with the result of discrete time conversion. Because controllers with fractional exponents of s are not easy to design. This controller is applied to a standard secondary system and its performance is examined. Then, it applies to solenoid valve which is widely used in industrial field. A Luenberger's observer was designed to estimate the disturbance state and the observed state was applied to the fractional order controller. As a result, uniform and precise control performance was obtained. It was confirmed that the position error of the steady state is within 0.1 [%] and the rising time is within about 0.03 [s].

• Smart Structures and Systems
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• v.24 no.3
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• pp.303-317
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• 2019

Magneto-rheological fluid (MRF) rotatory dampers are normally used for controlling the constant rotation of machines and engines. In this research, such a device is proposed to act as variable stiffness device to alleviate the rotational oscillation existing in the many engineering applications, such as motor. Under such thought, the main purpose of this work is to characterize the nonlinear torque-angular displacement/angular velocity responses of an MRF based variable stiffness device in oscillatory motion. A rotational hysteresis model, consisting of a rotatory spring, a rotatory viscous damping element and an error function-based hysteresis element, is proposed, which is capable of describing the unique dynamical characteristics of this smart device. To estimate the optimal model parameters, a modified whale optimization algorithm (MWOA) is employed on the captured experimental data of torque, angular displacement and angular velocity under various excitation conditions. In MWOA, a nonlinear algorithm parameter updating mechanism is adopted to replace the traditional linear one, enhancing the global search ability initially and the local search ability at the later stage of the algorithm evolution. Additionally, the immune operation is introduced in the whale individual selection, improving the identification accuracy of solution. Finally, the dynamic testing results are used to validate the performance of the proposed model and the effectiveness of the proposed optimization algorithm.

• Journal of IKEEE
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• v.23 no.3
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• pp.844-851
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• 2019

As wearable technology is becoming more common and a part of our lives, there have been many efforts to offer various smart services with wearable devices, such as motion recognition, safety of driving, and so on. In this paper, we present a method that exploits the 9-axis inertial sensors embedded in a smartwatch to identify whether the user is a vehicle driver or not and to estimate the steering wheel turning direction in the vehicle. The system consists of three components: (i) position recognition, (ii) driver recognition, and (iii) steering-wheel turning detection components. We have developed a prototype system for detecting user's motion with Arduino boards and IMU sensors. Our experiments show high accuracy in recognizing the driver and in estimating the wheel rotation angle. The average experimental error was $11.77^{\circ}$ which is small enough to perceiver the turning direction of steering-wheel.

• The Journal of the Acoustical Society of Korea
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• v.38 no.2
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• pp.154-159
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• 2019

When measuring the radiating noise of an underwater moving source, the range information between the acoustic source and the receiver is an important evaluation factor, and the measurement standards such as a receiver position, a moving source depth and a speed are set. Although there is a method of using the cross correlation as a method of finding the range of the underwater moving source, this method requires a time synchronization process. In this paper, we proposed the method to estimate the range by comparing the Doppler frequency difference of the theoretically calculated multipath signal with the Doppler frequency difference of the multipath signal estimated from the received signal. The proposed method does not require a separate time synchronization process. Simulations were performed to verify the performance, and the ranging error of the proposed method reduced by about 95 % than that of the conventional method.

• Journal of IKEEE
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• v.23 no.1
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• pp.1-8
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• 2019

In this paper, the electrical characteristics with various C-rates are tested with a high power series battery pack comprised of 18650 cylindrical nickel cobalt aluminum(NCA) lithium-ion battery. The electrical characteristics of discharge capacity test with 14S1P battery pack and electric vehicle (EV) cycle test with 4S1P battery pack are compared and analyzed by the various of C-rates. Multiple linear regression is used to estimate voltage imbalance of 14S1P and 4S1P battery packs with various C-rates based on experimental data. The estimation accuracy is evaluated by root mean square error(RMSE) to validate multiple linear regression. The result of this paper is contributed that to use for estimating the voltage imbalance of discharge capacity test with 14S1P battery pack using multiple linear regression better than to use the voltage imbalance of EV cycle with 4S1P battery pack.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.1-8
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• 2019

Developing reliable soil moisture prediction techniques at agricultural regions is a pivotal issue for sustaining stable crop productions. In this study, a physically-based SWAP(Soil-Water-Atmosphere-Plant) model was suggested to estimate soil moisture dynamics at the study sites. ROSETTA was also integrated to derive the soil hydraulic properties(${\alpha}$, n, ${\Theta}_r$, ${\Theta}_s$, $K_s$) as the input variables to SWAP based on the soil information(Sand, Silt and Clay-SSC, %). In order to predict the soil moisture dynamics in future, the mid-term TIGGIE(THORPEX Interactive Grand Global Ensemble) and long-term S2S(Subseasonal to Seasonal) weather forecasts were used, respectively. Our proposed approach was tested at the six study sites of RDA(Rural Development Administration). The estimated soil moisture values based on the SWAP model matched the measured data with the statistics of Root Mean Square Error(RMSE: 0.034~0.069) and Temporal Correlation Coefficient(TCC: 0.735~0.869) for validation. When we predicted the mid-/long-term soil moisture values using the TIGGE(0~15 days)/S2S(16~46 days) weather forecasts, the soil moisture estimates showed less variations during the TIGGE period while uncertainties were increased for the S2S period. Although uncertainties were relatively increased based on the increased leading time of S2S compared to those of TIGGE, these results supported the potential use of TIGGE/S2S forecasts in evaluating agricultural drought. Our proposed approach can be useful for efficient water resources management plans in hydrology, agriculture, etc.