• Economic and Environmental Geology
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• v.30 no.5
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• pp.469-476
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• 1997

In the conventional velocity analysis, the peaks of a semblance panel are picked and the stacking velocities of the peaks are assumed as RMS velocities from which interval velocities are determined. This velocity analysis technique is correct only for horizontal homogeneous layes and incurs error in a layer whose velocity varies laterally. Tediousness of peak picking and error in velocity analysis can be reduced by automatic velocity analysis techniques. An automatic velocity analysis algorithm has been presented in order to improve these problems by considering the stacking velocity from the view point of interval velocity model and by relating the stacking velocity and the interval velocity with the traveltimes. In this paper, we apply the automatic velocity analysis method to simple models having lateral velocity anomaly to verify the effectivenesses and limits of this method. From the results of numerical experiments, we can determine the interval velocites without pickings of the stacking velocities in the one-dimensional velocity analysis and the general patterns of the laterally varying interval velocities appear in the two-dimensional case. However, the interval velocity and the depth of velocity anomaly determined by two-dimensional automatic velocity analysis are somewaht discrepant in those of the theoretical model.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.638-642
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• 2005

For gas hydrate exploration, long offset multichannel seismic data acquired using by the 4km streamer length in Ulleung basin of the East Sea. The dataset was processed to define the BSRs (Bottom Simulating Reflectors) and to estimate the amount of gas hydrates. Confirmation of the presence of Bottom Simulating reflectors (BSR) and investigation of its physical properties from seismic section are important for gas hydrate detection. Specially, faster interval velocity overlying slower interval velocity indicates the likely presences of gas hydrate above BSR and free gas underneath BSR. In consequence, estimation of correct interval velocities and analysis of their spatial variations are critical processes for gas hydrate detection using seismic reflection data. Using Dix's equation, Root Mean Square (RMS) velocities can be converted into interval velocities. However, it is not a proper way to investigate interval velocities above and below BSR considering the fact that RMS velocities have poor resolution and correctness and the assumption that interval velocities increase along the depth. Therefore, we incorporated Migration Velocity Analysis (MVA) software produced by Landmark CO. to estimate correct interval velocities in detail. MVA is a process to yield velocities of sediments between layers using Common Mid Point (CMP) gathered seismic data. The CMP gathered data for MVA should be produced after basic processing steps to enhance the signal to noise ratio of the first reflections. Prestack depth migrated section is produced using interval velocities and interval velocities are key parameters governing qualities of prestack depth migration section. Correctness of interval velocities can be examined by the presence of Residual Move Out (RMO) on CMP gathered data. If there is no RMO, peaks of primary reflection events are flat in horizontal direction for all offsets of Common Reflection Point (CRP) gathers and it proves that prestack depth migration is done with correct velocity field. Used method in this study, Tomographic inversion needs two initial input data. One is the dataset obtained from the results of preprocessing by removing multiples and noise and stacked partially. The other is the depth domain velocity model build by smoothing and editing the interval velocity converted from RMS velocity. After the three times iteration of tomography inversion, Optimum interval velocity field can be fixed. The conclusion of this study as follow, the final Interval velocity around the BSR decreased to 1400 m/s from 2500 m/s abruptly. BSR is showed about 200m depth under the seabottom

• Journal of Animal Environmental Science
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• v.8 no.2
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• pp.73-78
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• 2002

The experiment was carried out to investigate the optimal air velocity for improving the ventilation efficiency of duct ventilation system used in Korean swine building. The results are followed ; In 2.2 m height of duct, the air velocity of hole was 5.0 m/s as the over level of recommendation. In different hole interval, the air velocity was various of 4.6${\sim}$11.6 m/s in narrow hole interval, 5.4${\sim}$10.9 m/s in broad hole interval. But the air velocity was 6.6${\sim}$7.7 m/s in duct system pierced hole with equal interval, and it was equal velocity in different parts of duct in this hole interval.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.31-39
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• 2010

The goal of automatic velocity analysis is to extract accurate velocity from voluminous seismic data with efficiency. In this study, we developed an efficient automatic velocity analysis algorithm by using bootstrapped differential semblance (BDS) and Monte Carlo inversion. To estimate more accurate results from automatic velocity analysis, the algorithm we have developed uses BDS, which provides a higher velocity resolution than conventional semblance, as a coherency estimator. In addition, our proposed automatic velocity analysis module is performed with a conditional initial velocity determination step that leads to enhanced efficiency in running time of the module. A new optional root mean square (RMS) velocity constraint, which prevents picking false peaks, is used. The developed automatic velocity analysis module was tested on a synthetic dataset and a marine field dataset from the East Sea, Korea. The stacked sections made using velocity results from our algorithm showed coherent events and improved the quality of the normal moveout-correction result. Moreover, since our algorithm finds interval velocity ($\nu_{int}$) first with interval velocity constraints and then calculates a RMS velocity function from the interval velocity, we can estimate geologically reasonable interval velocities. Boundaries of interval velocities also match well with reflection events in the common midpoint stacked sections.

• Journal of Environmental Science International
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• v.16 no.4
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• pp.449-458
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• 2007

Research results for the pressure drop variance depending on operation conditions such as change of inlet concentration, pulse interval, and face velocity, etc., in a pulse air jet-type bag filter show that while at $3kg/cm^2$ whose pulse pressure is low, it is good to make an pulse interval longer in order to form the first layer, it may not be applicable to industry because of a rapid increase in pressure. In addition, the change of inlet concentration contributes more to the increase of pressure drop than the pulse interval does. In order to reduce operation costs by minimizing filter drag of a filter bag at pulse pressure $5kg/cm^2$, the dust concentration should be minimized, and when the inlet dust loading is a lower concentration, the pulse interval in the operation should be less than 70 sec, but when inlet dust loading is a higher concentration, the pulse interval should be below 30 sec. In particular, in the case that inlet dust loading is a higher concentration, a high-pressure distribution is observed regardless of pulse pressure. This is because dust is accumulated continuously in the filter bag and makes it thicker as filtration time increases, and thus the pulse interval should be set to below 30 sec. If the equipment is operated at 1m/min of face velocity, while pressure drop is low, the bag filter becomes larger and thus, its economics are very low due to a large initial investment. Therefore, a face velocity of around 1.5 m/min is considered to be the optimal operation condition. At 1.5 m/min considered to be the most economical face velocity, if the pulse interval increases, since the amount of variation in filter drag is large, depending on the amount of inlet dust loading, the operation may be possible at a lower concentration when the pulse interval is 70 sec. However, for a higher concentration, either face velocity or pulse interval should be reduced.

• Journal of Environmental Science International
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• v.23 no.12
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• pp.2045-2056
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• 2014

In this study, pressure drop was measured in the pulse jet bag filter without venturi on which 16 numbers of filter bags (Ø$140{\times}850{\ell}$) are installed according to operation condition(filtration velocity, inlet dust concentration, pulse pressure, and pulse interval) using coke dust from steel mill. The obtained 180 pressure drop test data were used to predict pressure drop with multiple regression model so that pressure drop data can be used for effective operation condition and as basic data for economical design. The prediction results showed that when filtration velocity was increased by 1%, pressure drop was increased by 2.2% which indicated that filtration velocity among operation condition was attributed on the pressure drop the most. Pressure was dropped by 1.53% when pulse pressure was increased by 1% which also confirmed that pulse pressure was the major factor affecting on the pressure drop next to filtration velocity. Meanwhile, pressure drops were found increased by 0.3% and 0.37%, respectively when inlet dust concentration and pulse interval were increased by 1% implying that the effects of inlet dust concentration and pulse interval were less as compared with those changes of filtration velocity and pulse pressure. Therefore, the larger effect on the pressure drop the pulse jet bag filter was found in the order of filtration velocity($V_f$), pulse pressure($P_p$), inlet dust concentration($C_i$), pulse interval($P_i$). Also, the prediction result of filtration velocity, inlet dust concentration, pulse pressure, and pulse interval which showed the largest effect on the pressure drop indicated that stable operation can be executed with filtration velocity less than 1.5 m/min and inlet dust concentration less than $4g/m^3$. However, it was regarded that pulse pressure and pulse interval need to be adjusted when inlet dust concentration is higher than $4g/m^3$. When filtration velocity and pulse pressure were examined, operation was possible regardless of changes in pulse pressure if filtration velocity was at 1.5 m/min. If filtration velocity was increased to 2 m/min. operation would be possible only when pulse pressure was set at higher than $5.8kgf/cm^2$. Also, the prediction result of pressure drop with filtration velocity and pulse interval showed that operation with pulse interval less than 50 sec. should be carried out under filtration velocity at 1.5 m/min. While, pulse interval should be set at lower than 11 sec. if filtration velocity was set at 2 m/min. Under the conditions of filtration velocity lower than 1 m/min and high pulse pressure higher than $7kgf/cm^2$, though pressure drop would be less, in this case, economic feasibility would be low due to increased in installation and operation cost since scale of dust collection equipment becomes larger and life of filtration bag becomes shortened due to high pulse pressure.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.1010-1018
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• 2009

An inertial navigation system for pedestrian position tracking is proposed, where the position is computed using inertial sensors mounted on shoes. Inertial navigation system(INS) errors increase with time due to inertial sensor errors, and therefore it needs to reset errors frequently. During normal walking, there is an almost periodic zero velocity instance when a foot touches the floor. Using this fact, estimation errors are reduced and this method is called the zero velocity updating algorithm. When implementing this zero velocity updating algorithm, it is important to know when is the zero velocity interval. The gait states are modeled as a Markov process and each state is estimated using the hidden Markov model smoother. With this gait estimation, the zero or nearly zero velocity interval is more accurately estimated, which helps to reduce the position estimation error.

• The Korean Journal of Malacology
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• v.30 no.3
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• pp.273-279
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• 2014

The mass mortalities have been occurring of Korean scallop Patinopecten yessoensis from 1997's to now in Korea east sea. Cages behavior and interference effect (common name; curtain effect) between scallop cages were investigated in culture grounds on the eastern coastal waters of Korea for understand to mechanism of rising about mass mortalities of Korean scallop quickly. The first experiment was carried out in circulating water channel to assess inclination angel from relationship between velocity and cages interval, velocity with culture cages position. An angle of inclination of scallop culture cages were 94.6 to 92.3 degree under a several velocity which were from 0.1 m/s and 131.9 to 118.1 degree under 0.5 m/s with cages interval were 1 m, 94.3 to 91.0 degree under velocity is 0.1 m/s and 133.2 to 122.4 degree under 0.5 m/s with cages interval were 1.5 m and 94.6 to 96.4 degree under velocity is 0.1 m/s and 131.7 to 131.8 under 0.5 m/s with cages interval were 2 m. The second experiment was designed to prove the tank test. Velocities were measured inside and outside of the scallop culture ground at eastern sea of Korea. The velocity of inside of the culture was the slowest as 0.1m/s. In this result, interference between former cage and after cage was occurred.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.1
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• pp.33-42
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• 2020

In Integration Approach (IA)-based Pedestrian Dead Reckoning (PDR), it is important to detect the exact zero-velocity of the foot with an Inertial Measurement Unit (IMU). By detecting zero-velocity during the stance phase of the foot touching the ground and executing Zero-velocity UPdaTe (ZUPT) at the exact time, stable navigation information can be provided by the PDR. When the pace is fast, however, it is not easy to accurately detect the zero-velocity because of the small stance phase interval and the large signal variance of the corresponding interval. Incorrect zero-velcity detection greatly causes navigation errors of IA-based PDR. In this paper, we propose a method to detect the zero-velocity stably even at high speed by novel buffering of IMU's output data and signal processing of the buffer. And we design a PDR based on this. By analyzing the performance of the proposed Zero-Velocity Detection (ZVD) algorithm and ZVD-based PDR through experiemnts, we confirm that the proposed method can provide accurate navigation information of pedestrians such as firefighters in the indoor space.

• Journal of The Korean Astronomical Society
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• v.42 no.3
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• pp.61-69
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• 2009

Transverse velocity vectors can be determined from a pair of images successively taken with a time interval using an optical flow technique. We have tested the performance of the new technique called NAVE (non-linear affine velocity estimator) recently implemented by Chae & Sakurai using real image data taken by the Narrowband Filter Imager (NFI) of the Solar Optical Telescope (SOT) aboard the Hinode satellite. We have developed two methods of estimating the errors in the determination of velocity vectors, one resulting from the non-linear fitting ${\sigma}_{\upsilon}$ and the other ${\epsilon}_u$ resulting from the statistics of the determined velocity vectors. The real error is expected to be somewhere between ${\sigma}_{\upsilon}$ and ${\epsilon}_u$. We have investigated the dependence of the determined velocity vectors and their errors on the different parameters such as the critical speed for the subsonic filtering, the width of the localizing window, the time interval between two successive images, and the signal-to-noise ratio of the feature. With the choice of $v_{crit}$ = 2 pixel/step for the subsonic filtering, and the window FWHM of 16 pixels, and the time interval of one step (2 minutes), we find that the errors of velocity vectors determined using the NAVE range from around 0.04 pixel/step in high signal-to-noise ratio features (S/N $\sim$ 10), to 0.1 pixel/step in low signa-to-noise ratio features (S/N $\sim$ 3) with the mean of about 0.06 pixel/step where 1 pixel/step corresponds roughly to 1 km/s in our case.