• The Journal of the Acoustical Society of Korea
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• v.21 no.3E
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• pp.126-131
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• 2002

Ultrasonic measurements are made in egg white to study the properties of the solution of the natural protein. The high-Q ultrasonic resonator method is used to get the ultrasonic absorption spectra over the range 0.2-10 ㎒ at 20℃. It is proportional to the 1.25th power of the frequency. The gelation process caused by heat is studied from the change in the velocity and the absorption. at 3 ㎒ using the pulse echo overlap technique over the range of 10-80℃. The absorption decreases with increasing temperature up to 60℃ where it turns up sharply and rapidly increases thereafter. The strong absorption in the gel region is described by the interaction between the solution and the network structure made of protein. Very slow variation in time elapse is observed after the temperature is quickly raised. It would be a real-time observation of the network building process and the characteristic time for the process is shown to be 400 min. A hysteresis phenomenon with respect to the temperature is observed. This phenomenon is associated with the memorizing effect of the network structure of protein of the gel.

• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.53-66
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• 2021

Velocity model building is an essential procedure in seismic data processing. Conventional techniques, such as traveltime tomography or velocity analysis take longer computational time to predict a single velocity model and the quality of the inversion results is highly dependent on human expertise. Full-waveform inversions also depend on an accurate initial model. Recently, deep neural network techniques are gaining widespread acceptance due to an increase in their integration to solving complex and nonlinear problems. This study investigated cases of seismic velocity model building using deep neural network techniques by classifying items according to the neural networks used in each study. We also included cases of generating training synthetic velocity models. Deep neural networks automatically optimize model parameters by training neural networks from large amounts of data. Thus, less human interaction is involved in the quality of the inversion results compared to that of conventional techniques and the computational cost of predicting a single velocity model after training is negligible. Additionally, unlike full-waveform inversions, the initial velocity model is not required. Several studies have demonstrated that deep neural network techniques achieve outstanding performance not only in computational cost but also in inversion results. Based on the research results, we analyzed and discussed the characteristics of deep neural network techniques for building velocity models.

• Journal of The Korean Astronomical Society
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• v.47 no.6
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• pp.293-302
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• 2014

Simultaneous time monitoring observations of $H_2O$ $6_{16}-5_{23}$, SiO J = 1-0, 2-1, 3-2, and $^{29}SiO$ ${\upsilon}=0$, J = 1-0 lines are carried out in the direction of the Mira variable star TX Cam with the Korean VLBI Network single dish radio telescopes. For the first time, the $H_2O$ maser emission from TX Cam is detected near the stellar velocity at five epochs from April 10, 2013 (${\phi}=3.13$) to June 4, 2014 (${\phi}=3.89$) including minimum optical phases. The intensities of $H_2O$ masers are very weak compared to SiO masers. The variation of peak antenna temperature ratios among SiO ${\upsilon}=1$, J = 1-0, J = 2-1, and J = 3-2 masers is investigated according to their phases. The shift of peak velocities of $H_2O$ and SiO masers with respect to the stellar velocity is also investigated according to observed optical phases. The $H_2O$ maser emission occurs around the stellar velocity during our monitoring interval. On the other hand, the peak velocities of SiO masers show a spread compared to the stellar velocity. The peak velocities of SiO J = 2-1, and J = 3-2 masers show a smaller spread with respect to the stellar velocity than those of SiO J = 1-0 masers. These simultaneous observations of multi-frequencies will provide a good constraint for maser pumping models and a good probe for investigating the stellar atmosphere and envelope according to their different excitation conditions.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.768-773
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• 2011

The prediction method of future events using the time histories of velocity or pressure, etc., is a useful way for controlling various air vehicles. For example, the sensors of velocity or pressure can be used to extract the time mode coefficients of eigenmode of flow field, and then the result is applied to suppress wake or drag. The velocity information is mapped to the entire flow field, so this mapping function can be used to predict the future events based on the current information. The mapping function is composed of the huge amount of weight parameters, so the efficient way of finding these parameters is needed. Here, the neural network algorithm is studied to draw a mapping function using the number and location of velocity sensors.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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• pp.445-450
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• 2015

In the moving target problem, the velocity information of the moving target is very important as well as the high accuracy position information. To solve this problem, active researches are being conducted recently with combine the Time Difference of Arrival (TDOA) and Frequency Delay of Arrival(FDOA) measurements. However, since the FDOA measurement is utilizing the Doppler effect due to the relative velocity between the target source and the receiver sensor, it may be difficult to use the FDOA measurement if the moving target speed is not sufficiently fast. In this paper, we propose a method for estimating the position and the velocities of the target by using only the TDOA measurements for the low speed moving target in the indoor environment with sensor network. First, the target position and heading angle are obtained from the estimated positions of two attached transmitters on the target. Then, the target angular and linear velocities are also estimated. In addtion, we apply the Instrumental Variable (IV) technique to compensate the estimation error of the estimated target velocity. In simulation, the performance of the proposed algorithm is verified.

• Computers and Concrete
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• v.4 no.6
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• pp.477-497
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• 2007

The ultrasonic pulse velocity method has been widely used to evaluate the quality of concrete and assess the structural integrity of concrete structures. But its use for predicting strength is still limited since there are many variables affecting the relationship between strength and pulse velocity of concrete. This study is focused on establishing a complicated correlation between known input data, such as pulse velocity and mixture proportions of concrete, and a certain output (compressive strength of concrete) using artificial neural networks (ANN). In addition, the results predicted by the developed multilayer perceptrons (MLP) networks are compared with those by conventional regression analysis. The result shows that the correlation between pulse velocity and compressive strength of concrete at various ages can be well established by using ANN and the accuracy of the estimates depends on the quality of the information used to train the network. Moreover, compared with the conventional approach, the proposed method gives a better prediction, both in terms of coefficients of determination and root-mean-square error.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.139-142
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• 1996

This paper presents a method of controlling contact force for deburring tasks. The cope with the nonlinearities and time-varying properties of the robot and the environment, a neural network control theory is applied to design the contact force control system. We show that the contact force between the hand and the contacting surface can be controlled by adjusting the command velocity of a robot hand, which is accomplished by the modeling of a robot and the environment as Mass-Spring-Damper system. Simulation results are shown.

• Journal of Korean Society on Water Environment
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• v.32 no.4
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• pp.367-374
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• 2016

In this study, the RDII impact on sewer designing in the upstream monitoring area (A site) was considered. Based on the long-term (1/1/2011~12/31/2011) rainfall and flow data consisting of 10-min interval sampling in the nearby design area (B site), the maximum RDII/DWF ratio was selected. The sewer network system at B site was evaluated by the Manning equation. Scenario 1 considering the hourly maximum flow with respect to the flow velocity showed that none of the sewer pipes satisfied the minimum flow velocity condition (0.6 m/s), and 40 pipes did not achieve half of the velocity condition. In scenario 2 considering I/I, 1 the pipes satisfied 0.6 m/s, and 35 pipes showed 0.3 m/s. Scenario 3 reflected the effect of RDII. Velocities in 26 pipes were less than 0.3 m/s, and 4 pipes satisfied the velocity condition. With respect to the allowance rate, 17 pipes were shown to have more than 99%, and none of the pipes satisfied less than 95% of the allowance rate in scenario 1. In scenario 2, 17 Ed: Per the Table pipes showed more than 99% and one pipe showed less than 95%. In scenario 3, 16 pipes showed more than 99% of the allowance rate, and 19 pipes showed less than 95%. Based on these results, it is predicted that deposition would occur due to the slow flow velocity; however, capacity would not be a problem.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.7
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• pp.921-929
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• 2003

An experimental investigation was performed to study the characteristics of turbulent water flow in a horizontal circular tube by using liquid crystal. To determine some characteristics of the turbulent flow, 2D PIV technique is employed for velocity measurement and liquid crystal is used for heat transfer experiments in water. Temperature visualization was made quantitatively by calibrating the color of the liquid crystal versus temperature using various approaches (TLC technique: Thermochromic Liquid Crystal), and a neural-network algorithm was applied to the color-to-temperature calibration. This study shoud the temperature and time-mean velocity distribution for Re = 2,436, 2,500 and 2,724 along longitudinal sections and the results appear to be physically reasonable.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.2 s.173
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• pp.455-463
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• 2000

In order to develop nondestructive techniques for the quantitative estimation of creep damage a series of crept copper samples were prepared and their ultrasonic velocities were measured. Velocities measured in three directions with respect to the loading axis decreased nonlinearly and their anisotropy increased as a function of creep-induced porosity. A progressive damage model was described to explain the void-velocity relationship, including the anisotropy. The comparison of modeling study showed that the creep voids evolved from sphere toward flat oblate spheroid with its minor axis aligned along the stress direction. This model allowed us to determine the average aspect ratio of voids for a given porosity content. A novel technique, the back propagation neural network (BPNN), was applied for estimating the porosity content due to the creep damage. The measured velocities were used to train the BP classifier, and its accuracy was tested on another set of creep samples containing 0 to 0.7 % void content. When the void aspect ratio was used as input parameter together with the velocity data, the NN algorithm provided much better estimation of void content.