• Atmosphere
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• v.29 no.1
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• pp.105-112
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• 2019

Measurements of vertical air motion and microphysics are essential for improving our understanding of convective clouds. In this paper, the author reviews the current research on the retrieval of vertical air motions using the cloud radar. At radar wavelengths of 3 mm (W-band radar; 94-GHz radar; cloud radar), the raindrop backscattering cross-section (${\sigma}b$) varies between successive maxima and minima as a function of the raindrop diameter (D) that are well described by Mie theory. The first Mie minimum in the backscattering cross-section occurs at D~1.68 mm, which translates to a raindrop terminal fall velocity of ${\sim}5.85m\;s^{-1}$ based on the Gunn and Kinzer relationship. Since raindrop diameters often exceed this size, the signal is captured in the radar Doppler spectrum, and thus, the location of the first Mie minimum can be used as a reference for retrieving the vertical air motion. The Mie technique is applied to radar Doppler spectra from the surface-based and airborne, upward pointing W-band radars. The contributions of aircraft motion to the vertical air motion are also described and further the first-order aircraft motion corrected equation is presented. The review also shows that the separate spectral peaks due to the cloud droplets can provide independent validation of the Mie technique retrieved vertical air motion using the cloud droplets as a tracer of vertical air motion.

• The Korean Journal of Nuclear Medicine Technology
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• v.26 no.1
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• pp.33-37
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• 2022

Purpose ¹⁸F-Florbetaben (FBB) Readings are made by visually comparing the signal strengths of gray matter and white matter. We intend to evaluate the usefulness of image analysis by comparing quantified image analysis with readout. Materials and Methods Based on the reading results, 100 patients were divided into a negative scan and a positive scan, and 300 MBq of FBB was injected, and images were taken 90 minutes later for 20 minutes. The equipment was a Discovery 600 (GE Healthcare, MI, USA). Four regions of interest (lateral temporal lobes, frontal lobes, posterior cingulate & precuneus, and parietal lobes) were established based on the amyloid reading standard provided by the manufacturer. For image analysis, SUVratio (SUVr) was calculated by dividing each SUVmean by the cerebellum, and the average SUVr in the entire area was performed. Statistical analysis analyzed the cutoff derivation through ROC Curve, the difference between groups in Independent sample t-test, and the degree of agreement with the reading result through Kappa test. Results The average SUVr cutoff in the entire area was 1.23. Concordance with the read results using cutoff was 95/100 (95%) for negative and 92/100 (92%) for positive. As a result of the t-test, there was a statistically significant difference between the groups (P < 0.05), and the Kappa statistical result showed a high degree of agreement with 0.867 (P < 0.05). Conclusion The results of image analysis were statistically significant and showed a high degree of agreement with the reading results. In addition, FBB image analysis can be viewed by 3D mapping the area where amyloid is accumulated, location estimation is possible, and quantitative analysis results can be viewed in detail. If quantified FBB image analysis is used as an auxiliary indicator, it is thought to be helpful in reading.

• Journal of Animal Science and Technology
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• v.46 no.4
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• pp.537-546
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• 2004

Adiponectin is adipocyte complement-related protein which is highly specialized to play important roles in metabolic and honnonal processes. This protein, called GBP-28, AdipoQ, and Acrp30, is encoded by the adipose most abundant gene transcript 1 (APM1) which locates on human chromosome 3q27 and mouse chromosome 16. In order to determine chromosomal localization of the porcine APM1, we carried out PCR analysis using somatic cell hybrid panel as well as porcine whole genome radiation hybrid (RH) panel. The result showed that the porcine APM1 located on chromosome 13q41 or 13q46-49. These locations were further investigated with the two point analysis of RH panel, revealed the most significant linked marker (LOD score 20.29) being SIAT1 (8 cRs away), where the fat-related QTL located. From the SSCP analysis of APM1 using 8 pig breeds, two distinct SSCP types were detected from K~ native and Korean wild pigs. The determined sequences in Korean native and Korean wild pigs showed that two nucleotide positions (T672C and C705G) were substituted. The primary sequence of the porcine APM1 has 79 to 87% identity with those of human, mouse, and bovine APM1. The domain structures of the porcine APM1 such as signal sequence, hypervariable region, collagenous region. and globular domain are also similar to those of mammalian genes.

• Investigative Magnetic Resonance Imaging
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• v.13 no.1
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• pp.31-39
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• 2009

Purpose : This study proposes the keyhole method in order to improve the time resolution of the proton resonance frequency(PRF) MR temperature monitoring technique. The values of Root Mean Square (RMS) error of measured temperature value and Signal-to-Noise Ratio(SNR) obtained from the keyhole and full phase encoded temperature images were compared. Materials and Methods : The PRF method combined with GRE sequence was used to get MR temperature images using a clinical 1.5T MR scanner. It was conducted on the tissue-mimic 2% agarose gel phantom and swine's hock tissue. A MR compatible coaxial slot antenna driven by microwave power generator at 2.45GHz was used to heat the object in the magnetic bore for 5 minutes followed by a sequential acquisition of MR raw data during 10 minutes of cooling period. The acquired raw data were transferred to PC after then the keyhole images were reconstructed by taking the central part of K-space data with 128, 64, 32 and 16 phase encoding lines while the remaining peripheral parts were taken from the 1st reference raw data. The RMS errors were compared with the 256 full encoded self-reference temperature image while the SNR values were compared with the zero filling images. Results : As phase encoding number at the center part on the keyhole temperature images decreased to 128, 64, 32 and 16, the RMS errors of the measured temperature increased to 0.538, 0.712, 0.768 and 0.845$^{\circ}C$, meanwhile SNR values were maintained as the phase encoding number of keyhole part is reduced. Conclusion : This study shows that the keyhole technique is successfully applied to temperature monitoring procedure to increases the temporal resolution by standardizing the matrix size, thus maintained the SNR values. In future, it is expected to implement the MR real time thermal imaging using keyhole method which is able to reduce the scan time with minimal thermal variations.

• Progress in Medical Physics
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• v.19 no.4
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• pp.269-275
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• 2008

Although MR imaging is generally applicable to depict knee joint deterioration it, is sometimes occurred to mis-read and mis-diagnose the common knee joint diseases. In this study, we employed magnetization transfer ratio (MTR) method to improve the diagnosis of the various knee joint diseases. Spin-echo (SE) T2-weighted images (TR/TE 3,400-3,500/90-100 ms) were obtained in seven cases of knee joint deterioration, FSE T2-weighted images (TR/TE 4,500-5,000/100-108 ms) were obtained in seven cases of knee joint deterioration, gradient-echo (GRE) T2-weighted images (TR/TE 9/4.56/$50^{\circ}$ flip angle, NEX 1) were obtained in 3 cases of knee joint deterioration, In six cases of knee joint deterioration, fat suppression was performed using a T2-weighted short T1/tau inverse recovery (STIR) sequence (TR/TE =2,894-3,215 ms/70 ms, NEX 3, ETL 9). Calculation of MTR for individual pixels was performed on registration of unsaturated and saturated images. After processing to make MTR images, the images were displayed in gray color. For improving diagnosis, three-dimensional isotropic volume images, the MR tristimulus color mapping and the MTR map was employed. MTR images showed diagnostic images quality to assess the patients' pathologies. The intensity difference between MTR images and conventional MRI was seen on the color bar. The profile graph on MTR imaging effect showed a quantitative measure of the relative decrease in signal intensity due to the MT pulse. To diagnose the pathologies of the knee joint, the profile graph data was shown on the image as a small cross. The present study indicated that MTR images in the knee joint were feasible. Investigation of physical change on MTR imaging enables to provide us more insight in the physical and technical basis of MTR imaging. MTR images could be useful for rapid assessment of diseases that we examine unambiguous contrast in MT images of knee disorder patients.

• Investigative Magnetic Resonance Imaging
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• v.18 no.4
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• pp.303-313
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• 2014

Purpose : In-vivo conductivity reconstruction using transmit field ($B_1{^+}$) information of MRI was proposed. We assessed the accuracy of conductivity reconstruction in the presence of statistical noise in complex $B_1{^+}$ map and provided a parametric model of the conductivity-to-noise ratio value. Materials and Methods: The $B_1{^+}$ distribution was simulated for a cylindrical phantom model. By adding complex Gaussian noise to the simulated $B_1{^+}$ map, quantitative conductivity estimation error was evaluated. The quantitative evaluation process was repeated over several different parameters such as Larmor frequency, object radius and SNR of $B_1{^+}$ map. A parametric model for the conductivity-to-noise ratio was developed according to these various parameters. Results: According to the simulation results, conductivity estimation is more sensitive to statistical noise in $B_1{^+}$ phase than to noise in $B_1{^+}$ magnitude. The conductivity estimate of the object of interest does not depend on the external object surrounding it. The conductivity-to-noise ratio is proportional to the signal-to-noise ratio of the $B_1{^+}$ map, Larmor frequency, the conductivity value itself and the number of averaged pixels. To estimate accurate conductivity value of the targeted tissue, SNR of $B_1{^+}$ map and adequate filtering size have to be taken into account for conductivity reconstruction process. In addition, the simulation result was verified at 3T conventional MRI scanner. Conclusion: Through all these relationships, quantitative conductivity estimation error due to statistical noise in $B_1{^+}$ map is modeled. By using this model, further issues regarding filtering and reconstruction algorithms can be investigated for MREPT.

• Geophysics and Geophysical Exploration
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• v.6 no.3
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• pp.126-133
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• 2003

High-resolution shallow marine seismic surveys have been carried out for the resources exploration, engineering applications and Quaternary mapping. To improve the resolution of subsurface structure image, multichannel digital technique has been applied. The quality of the image depends on the vertical and horizontal resolution and signal to noise (S/N) ratio which are associated with the data acquisition parameters such as sample interval, common midpoint (CMP) interval and CMP fold. To understand the effect of the acquisition parameters, a test survey was carried out off Yeosu and the acquired data were analyzed. A 30 $in^3$ small air gun was used as a seismic source and 8 channel streamer cable with a 5 m group interval was used as a receiver. The data were digitally recorded with a shot interval of 2 s and sample interval of 0.1 ms. The acquired data were resampled with various sample intervals, CMP intervals and CMP folds. The resampled data were processed, plotted as seismic sections and compared each other. The analysis results show that thin bed structure with ${\~}1m$ thickness and ${\~}6^{\circ}$ slope can be imaged with good resolution and continuity and low noise using the acquisition parameters with a sample interval shorter than 0.2 ms, CMP interval shorter than 2.5 m and CMP fold more than 4. Because seismic resolution is associated with the acquisition parameters, the quality of the subsurface structure can be imaged successfully using suitable and optimum acquisition parameters.

• Progress in Medical Physics
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• v.20 no.4
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• pp.298-307
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• 2009

By using the MR T2 map technique, this study intends, first, to measure the change of T2 values of cartilage between healthy people and patients with osteoarthritis and, second, to assess the form and the damage of cartilage in the knee-joint, through which this study would consider the utility of the T2 map technique. Thirty healthy people were selected based on their clinical history and current status and another thirty patients with osteoarthritis of the knee who were screened by simple X-ray from November 2007 to December 2008 were selected. Their T2 Spin Echo (SE hereafter) images for the cartilage of the knee joint were collected by using the T2 SE sequence, one of the multi-echo methods (TR: 1,000 ms; TE values: 6.5, 13, 19.5, 26, 32.5. 40, 45.5, 52). Based on these images, the changes in the signal intensity (SI hereafter) for each section of the cartilage of the knee joint were measured, which yielded average values of T2 through the Origin 7.0 Professional (Northampton, MA 01060 USA). With these T2s, the independent samples T-test was performed by SPSS Window version 12.0 to run the quantitative analysis and to test the statistical significance between the healthy group and the patient group. Closely looking at T2 values for each anterior and lateral articular cartilage of the sagittal plane and the coronal plane, in the sagittal plane, the average T2 of the femoral cartilage in the patient group with arthritis of the knee ($42.22{\pm}2.91$) was higher than the average T2 of the healthy group ($36.26{\pm}5.01$). Also, the average T2 of the tibial cartilage in the patient group ($43.83{\pm}1.43$) was higher than the average T2 in the healthy group ($36.45{\pm}3.15$). In the case of the coronal plane, the average T2 of the medial femoral cartilage in the patient group ($45.65{\pm}7.10$) was higher than the healthy group ($36.49{\pm}8.41$) and so did the average T2 of the anterior tibial cartilage (i.e., $44.46{\pm}3.44$ for the patient group vs. $37.61{\pm}1.97$ for the healthy group). As for the lateral femoral cartilage in the coronal plane, the patient group displayed the higher T2 ($43.41{\pm}4.99$) than the healthy group did ($37.64{\pm}4.02$) and this tendency was similar in the lateral tibial cartilage (i.e., $43.78{\pm}8.08$ for the patient group vs. $36.62{\pm}7.81$ for the healthy group). Along with the morphological MR imaging technique previously used, the T2 map technique seems to help patients with cartilage problems, in particular, those with the arthritis of the knee for early diagnosis by quantitatively analyzing the structural and functional changes of the cartilage.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.