• Geophysics and Geophysical Exploration
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• v.25 no.3
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• pp.129-139
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• 2022

Analog-based streamers for ultra-high-resolution seismic surveys are capable of additional noise ingress in water, but the specifications cannot be expanded through interconnections. Foreign-produced digital streamers have been introduced and used primarily at domestic research institutes; however, the cost is high and smooth maintenance is challenging. This study investigates the localization of ultra-high-resolution digital streamers capable of high-resolution imaging of a geological structure. A digital streamer capable of 24-bit, 10 kHz digital sampling of up to 64 channel data was developed through research and development. Various quantitative specifications of the system were designed and developed close to the benchmark model, Geometrics' GeoEel streamer, and the number of modules that make up the system was drastically reduced, reducing development costs and making it easier to use. The field applicability of the developed streamer system was evaluated in an in situ experiment conducted in the waters around the Port of Yeong-il Bay in Pohang in April 2022.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.177-188
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• 2022

Recently, the application of distributed acoustic sensors (DAS), which can replace geophones and seismometers, has significantly increased along with interest in micro-seismic monitoring technique, which is one of the CO₂ storage monitoring techniques. A significant amount of temporally and spatially continuous data is recorded in a DAS monitoring system, thereby necessitating fast and accurate data processing techniques. Because event detection and seismic phase picking are the most basic data processing techniques, they should be performed on all data. In this study, a machine learning-based P, S wave phase picking algorithm was developed to compensate for the limitations of conventional phase picking algorithms, and it was modified using a transfer learning technique for the application of DAS data consisting of a single component with a low signal-to-noise ratio. Our model was constructed by modifying the convolution-based EQTransformer, which performs well in phase picking, to the ResUNet structure. Not only the global earthquake dataset, STEAD but also the augmented dataset was used as training datasets to enhance the prediction performance on the unseen characteristics of the target dataset. The performance of the developed algorithm was verified using K-net and KiK-net data with characteristics different from the training data. Additionally, after modifying the trained model to suit DAS data using the transfer learning technique, the performance was verified by applying it to the DAS field data measured in the Pohang Janggi basin.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.31-38
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• 2024

The digital phase-locked loop(DPLL) is one of the circuits composed of a digital detector, digital loop filter, voltage-controlled oscillator, and divider as a fundamental circuit, widely used in many fields such as electrical and circuit fields. A state estimator using various mathematical algorithms is used to improve the performance of a digital phase-locked loop. Traditional state estimators have utilized Kalman filters of infinite impulse response state estimators, and digital phase-locked loops based on infinite impulse response state estimators can cause rapid performance degradation in unexpected situations such as inaccuracies in initial values, model errors, and various disturbances. In this paper, we propose a two-layer Frobenius norm-based finite impulse state estimator to design a new digital phase-locked loop. The proposed state estimator uses the estimated state of the first layer to estimate the state of the first layer with the accumulated measurement value. To verify the robust performance of the new finite impulse response state estimator-based digital phase locked-loop, simulations were performed by comparing it with the infinite impulse response state estimator in situations where noise covariance information was inaccurate.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.307-321
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• 2016

Planetary Boundary Layer Height (PBLH) is a major input parameter for weather forecasting and atmosphere diffusion models. In order to estimate the sub-grid scale variability of PBLH, we need to monitor PBLH data with high spatio-temporal resolution. Accordingly, we introduce a LIdar observation VEhicle (LIVE), and analyze PBLH derived from the lidar loaded in LIVE. PBLH estimated from LIVE shows high correlations with those estimated from both WRF model ($R^2=0.68$) and radiosonde ($R^2=0.72$). However, PBLH from lidar tend to be overestimated in comparison with those from both WRF and radiosonde because lidar appears to detect height of Residual Layer (RL) as PBLH which is overall below near the overlap height (< 300 m). PBLH from lidar with 10 min time resolution shows typical diurnal variation since it grows up after sunrise and reaches the maximum after 2 hours of sun culmination. The average growth rate of PBLH during the analysis period (2014/06/26 ~ 30) is 1.79 (-2.9 ~ 5.7) m $min^{-1}$. In addition, the lidar signal measured from moving LIVE shows that there is very low noise in comparison with that from the stationary observation. The PBLH from LIVE is 1065 m, similar to the value (1150 m) derived from the radiosonde launched at Sokcho. This study suggests that LIVE can observe continuous and reliable PBLH with high resolution in both stationary and mobile systems.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.249-256
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• 2010

To the detect of SPIO-labelled hMSC, in vitro study on various cell concentration and in vivo molecular magnetic resonance imaging(MRI) technique using T2, $T2^*$ and SWI are compared with pathology. Cell concentration was $1.56{\times}10^4$, $3.13{\times}10^4$, $6.25{\times}10^4$, $1.25{\times}10^5$, $2.5{\times}10^5$, $5{\times}10^5\;cells/m{\ell}$ and for control $5{\times}10^5\cells/m{\ell}$. MRI technique using T2, $T^2$ and SWI. Photothrombotic infarction was located 2.5mm from bregma right, posterior. Cell injected through the tail vein of rat for 8 rats. MRI performed pre injection and post injection of 1, 3, 7 and 14days and sacrifice for pathology. MRI analysed on quantitatively. In vitro result, SWI was highest CNR as compared with $T2^*WI$, T2WI and $2.5{\times}10^5\;cells/m{\ell}$ cell concentration. In vivo result among the T2WI, $T2^WI$, SWI, T2WI is highest CNR between normal and infarction. CNR in normal-SPIO and infarction-SPIO is high score in SWI. Therefore, T2WI is good distinguish between normal and infarction, SWI are well detect SPIO-labelled hMSC from normal and infarction. Nowaday, SWI are mostly used on hemorrhage, calcification etc. in clinically, but for the future, stem cell therapy is commonly application at all disease which is good observing tool for SPIO-labelled stem cells.

• Economic and Environmental Geology
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• v.48 no.1
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• pp.25-39
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• 2015

$CO_2$ geological storage plays an important role in reduction of greenhouse gas emissions, but there is a lack of research for CCS demonstration. To achieve the goal of CCS, storing $CO_2$ safely and permanently in underground geological formations, it is essential to understand the characteristics of them, such as total storage capacity, stability, etc. and establish an injection strategy. We perform the impedance inversion for the seismic data acquired from the Ulleung Basin in 2012. To review the possibility of $CO_2$ storage, we also construct porosity models and extract attributes of the prospects from the seismic data. To improve the quality of seismic data, amplitude preserved processing methods, SWD(Shallow Water Demultiple), SRME(Surface Related Multiple Elimination) and Radon Demultiple, are applied. Three well log data are also analysed, and the log correlations of each well are 0.648, 0.574 and 0.342, respectively. All wells are used in building the low-frequency model to generate more robust initial model. Simultaneous pre-stack inversion is performed on all of the 2D profiles and inverted P-impedance, S-impedance and Vp/Vs ratio are generated from the inversion process. With the porosity profiles generated from the seismic inversion process, the porous and non-porous zones can be identified for the purpose of the $CO_2$ sequestration initiative. More detailed characterization of the geological storage and the simulation of $CO_2$ migration might be an essential for the CCS demonstration.

• Journal of Biomedical Engineering Research
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• v.25 no.5
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• pp.421-430
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• 2004

Clinically, it is almost impossible for a physician to distinguish subtle changes of frequency spectrum by using a stethoscope alone especially in the early stage of thrombus formation. Considering that reliability of mechanical valve is paramount because the failure might end up with patient death, early detection of valve thrombus using noninvasive technique is important. Thus the study was designed to provide a tool for early noninvasive detection of valve thrombus by observing shift of frequency spectrum of acoustic signals with computer aid diagnosis system. A thrombus model was constructed on commercialized mechanical valves using polyurethane or silicon. Polyurethane coating was made on the valve surface, and silicon coating on the sewing ring of the valve. To simulate pannus formation, which is fibrous tissue overgrowth obstructing the valve orifice, the degree of silicone coating on the sewing ring varied from 20％, 40％, 60％ of orifice obstruction. In experiment system, acoustic signals from the valve were measured using microphone and amplifier. The microphone was attached to a coupler to remove environmental noise. Acoustic signals were sampled by an AID converter, frequency spectrum was obtained by the algorithm of spectral analysis. To quantitatively distinguish the frequency peak of the normal valve from that of the thrombosed valves, analysis using a neural network was employed. A return map was applied to evaluate continuous monitoring of valve motion cycle. The in-vivo data also obtained from animals with mechanical valves in circulatory devices as well as patients with mechanical valve replacement for 1 year or longer before. Each spectrum wave showed a primary and secondary peak. The secondary peak showed changes according to the thrombus model. In the mock as well as the animal study, both spectral analysis and 3-layer neural network could differentiate the normal valves from thrombosed valves. In the human study, one of 10 patients showed shift of frequency spectrum, however the presence of valve thrombus was yet to be determined. Conclusively, acoustic signal measurement can be of suggestive as a noninvasive diagnostic tool in early detection of mechanical valve thrombosis.

• Geophysics and Geophysical Exploration
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• v.11 no.3
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• pp.242-249
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• 2008

Recently, AVO analysis has been widely used in oil exploration with seismic subsurface section as a direct indicator of the existence of the gas. In the case of the deep reservoirs like the gas reservoirs in the East-sea, it is often difficult to observe AVO responses in CMP gathers even though the bright spots are shown in the stacked section. Because the reservoir becomes more consolidated as its depth deepens, P-wave velocity does not decrease significantly when the pore fluid is replaced by the gas. Thus the difference in Poisson's ratio, which is a key factor for AVO response, between the reservoir and the layer above it does not increase significantly. In this study, we analyzed the effects of Poisson's ratio difference on AVO response with a variety of Poisson's ratios for the upper and lower layers. The results show that, as the difference in Poisson's ratio between the upper and lower layers decreases, the change in the reflection amplitude with incidence angle decreases and AVO responses become insignificant. To consider the limitation of AVO responses shown in the gas reservoir in East-sea, the velocity model was made by simulation Gorae V structure with seismic data and well logs. The results of comparing AVO responses observed from the synthetic data with theoretical AVO responses calculated by using material properties show that the amount of the change in reflection amplitude with increasing incident angle is very small when the difference in Poisson's ratio between the upper and lower layers is small. In addition, the characteristics of AVO responses were concealed by noise or amplitude distortion arisen during preprocessing. To overcome such limitations of AVO analysis of the data from deep reservoirs, we need to acquire precisely reflection amplltudes In data acquisition stage and use processing tools which preserve reflection amplitude in data processing stage.

• Journal of radiological science and technology
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• v.31 no.3
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• pp.287-292
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• 2008

Position information of radiation interactions in detection material is essential to reconstruct a radiation source image. With most position sensing techniques, the position information of a single interaction inside the detectors can be precisely obtained. Each interaction position of multi-scattering inside scintillators, however, can not be individually measured and only the average of the scattering positions can be obtained, which causes the uncertainty in the measured interaction position. In this paper, the position uncertainties due to the multi-scattering were calculated by Monte Carlo simulation. The simulation model was a 50 by 50 by 5 mm $LaCl_3$(Ce) scintillator(pixel size is 2 by 2 by 5mm) which was utilized for the dual collimation camera. The dual collimation camera uses the information from both photoelectric effect and Compton scattering, and therefore, position uncertainties for both partial and full energy deposition of radiation interactions are calculated. In the case of partial energy deposition(PED), the standard deviations of positions are less than $1{\sim}2mm$, which means the uncertainty caused by multi-scattering is not significant. Because the effect of the multi-scattering with PED is insignificant, the multi-scattering has little effect on the performance of Compton imaging of dual collimation camera. In the case of full energy deposition(FED), however, the standard deviation of the positions is about twice that of the pixel size of the 1stdetector, except for 122keV incident radiations. Therefore, the standard deviations caused by multi-scatterings should be considered in the design of the coded mask of the dual collimation camera to avoid artifact on the reconstructed image. The position uncertainties of the FEDs are much larger than those of the PEDs for all radiation energies and the ratio of PEDs to FEDs increases when the incident radiation energy increases. The position uncertainties of both PEDs and FEDs are dependent on the incident radiation energy.

• Journal of forensic and investigative science
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• v.1 no.2
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• pp.5-19
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• 2006

Data mining is an information extraction activity to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis. Law enforcement agencies deal with mass data to investigate the crime and its amount is increasing due to the development of processing the data by using computer. Now new challenge to discover knowledge in that data is confronted to us. It can be applied in criminal investigation to find offenders by analysis of complex and relational data structures and free texts using their criminal records or statement texts. This study was aimed to evaluate possibile application of data mining and its limitation in practical criminal investigation. Clustering of the criminal cases will be possible in habitual crimes such as fraud and burglary when using data mining to identify the crime pattern. Neural network modelling, one of tools in data mining, can be applied to differentiating suspect's photograph or handwriting with that of convict or criminal profiling. A case study of in practical insurance fraud showed that data mining was useful in organized crimes such as gang, terrorism and money laundering. But the products of data mining in criminal investigation should be cautious for evaluating because data mining just offer a clue instead of conclusion. The legal regulation is needed to control the abuse of law enforcement agencies and to protect personal privacy or human rights.