• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.244-247
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• 2022

In this paper, the performance of the decoding schemes using linear MMSE filters in the frequency and time domains and the reinforcement Gauss-Seidel algorithm for the orthogonal time frequency space (OTFS) system that can improve robustness under high-speed mobile environments are compared. The reinforcement Gauss-Seidel algorithm can improve the bit error rate performance by suppressing the noise enhancement. The simulation results show that the performance of the decoding scheme using the linear MMSE filter in the frequency domain is severely degraded due to the effect of Doppler shift as the mobile speed increases. In addition, the decoding scheme using the reinforcement Gauss-Seidel algorithm under the channel environment with 120 km/h and 500 km/h speeds outperforms the decoding schemes using linear MMSE filters in the frequency and time domains.

• Analytical Science and Technology
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• v.19 no.2
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• pp.142-148
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• 2006

To determine malachite green and leuco-malachite green residues in sea food, a liquid chromatographic method has been optimized. The target compounds were extracted in the homogenized edible tissues with a mixture of McIlvaine buffer-acetonitrile and partitioned against dichloromethane. After concentrating the lower layer, the resulting residues were re-dissolved in methanol and analyzed by the HPLC with visible detector at 620 nm using acetonitrile-acetate buffer. For the analysis of leuco-malachite green with malachite green simultaneously, post-column packed with lead(IV) oxide was used for oxidizing leuco-malachite green to malachite green. The correlation coefficients($r^2$) was 0.9989 for malachite green, and 0.9995 for leuco-malachite green. The limit of detection was 0.005 mg/kg for the combined of malachite green and leuco-malachite green at signal/noise${\geq}3$. The recovery rate was within a reliable range of 84~98% (CV 3~16%). Leuco-malachite green were detected in carp and crusian carp.

• Analytical Science and Technology
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• v.20 no.1
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• pp.84-90
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• 2007

To determine levels of 11 sulfonamides for animals in food, simultaneously, a selective method of high performance liquid chromatography with UV detector has been applied. The targets were sulfachlorpyridazine (SCP), sulfadiazine (SDZ), sulfadimethoxine (SDM), sulfisoxazole (SSX), sulfamerazine (SMZ), sulfamethazine (SMT), sulfamethoxazole (SMX), sulfamethoxypyridazine (SMP), sulfamonomethoxine (SMM), sulfaquinoxaline (SQX) and sulfathiazole (STZ). Food samples were beef, pork, chicken, milk and whole egg that were collected at the main 6 cities in Korea as Seoul, Busan, Daejon, Incheon, Mokpo and Gangneung. After homogenizing food samples with sodium phosphate solution and acetonitrile, it was extracted with n-hexane. The mobile phase gradient was a mixture of 5 mM potassium phosphate (pH 3.25) and methanol with a gradient ratio from 100:0 to 30:70. The UV wavelength was 270 nm. The overall recoveries were ranged from 75% to 95% and the limit of detection was minimum 0.004 mg/kg for SMT, and 0.007 mg/kg for STZ at signal/noise > 3, respectively. As results, sulfonamide drugs were not detected in most of the selected food samples, however, sulfamonomethoxine was detected in meat. The determined level of sulfamonomethoxine were 0.03 and 0.06 mg/kg for beef that were below the MRLs.

• Korean Journal of Radiology
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• v.25 no.9
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• pp.798-806
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• 2024

Objective: Dual-layer CT (DLCT) can create virtual monochromatic images (VMIs) at various monochromatic X-ray energies, particularly at low keV levels, with high contrast-to-noise ratio. The purpose of this study was to assess the clinical feasibility of contrast-enhanced chest DLCT with a low keV VMI for preoperative breast cancer staging, in comparison to breast MRI. Materials and Methods: A total of 152 patients with 155 index breast cancers were enrolled in the study. VMIs were generated from contrast-enhanced chest DLCT at 40 keV and maximum intensity projection (MIP) with three-dimensional (3D) reconstruction was performed for both bilateral breast areas. Two radiologists reviewed in consensus the 3D MIP images of the chest DLCT with VMI and breast MRI in separate sessions with a 3-month wash-out period. The detection rate and mean tumor size of the index cancer were compared between the chest DLCT with VMI and breast MRI. Additionally, the agreement of tumor size measurement between the two imaging modalities were evaluated. Results: Of all index cancers, 84.5% (131/155) were detected in the chest DLCT with VMI, while 88.4% (137/155) were detected in the breast MRI (P = 0.210). The Bland-Altman agreement between the chest DLCT with VMI and breast MRI was a mean difference of -0.05 cm with 95% limits of agreement of -1.29 to 1.19 cm. The tumor size in the chest DLCT with VMI (2.3 ± 1.7 cm) was not significantly different from that in the breast MRI (2.4 ± 1.6 cm) (P = 0.106). Conclusion: The feasibility of chest DLCT with VMI was demonstrated for preoperative tumor staging in breast cancer patients, showing comparable cancer detectability and good agreement in tumor size measurement compared to breast MRI. This suggests that chest DLCT with VMI can serve as a potential alternative for patients who have contraindications to breast MRI.

• Journal of Animal Science and Technology
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• v.49 no.5
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• pp.559-568
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• 2007

Recently, copy number variations (CNV) of genes or genomic segments have been intensively studied and various analysis methods have been developed. In this study, quantitative oligonucleotide ligation assay (qOLA) was applied to investigate CNV of KIT gene in the Landrace breed. A combined assay using qOLA and pyrosequencing, 6 genotype classes, I1/I1 or I3/i (IBe), I1/I2 or I3/IP, I1/I3, I1/IP or I2/i (IBe), I2/I2and I2/IP, were identified from 44 Landrace pigs. Genotype assignment using grouping features of measurements on a scatter plot showed 100% agreement with those using a statistical assignment by PROC FASTCLUS procedure implemented in the SAS package. Two versions (3100 and 3130) of ABI sequencers gave the same genotyping results, indicating there was no influence on qOLA by different versions of instrument, however, the means of standard deviation and coefficient of variation from the qOLA on a ABI 3130 (2.33 and 4.10) was lower than those from the qOLA on a ABI 3100 (2.67 and 4.81). Effect of proteinase K treatment on the PCR product followed by qOLA was very clear because noise peaks were disappeared and the observed ration fit better to the reference ratio corresponding to each genotype.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.04a
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• pp.5-9
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• 2004

In this paper, we studied the OCT(Optical Coherence Tomography) system which it has been extensively studied because of having some advantages such as high resolution cross-sectional images, low cost, and small size configuration. A basic principle of OCT system is Michelson interferometer. The characteristics of light source determine the resolution and the transmission depth. As a results, the light source have a commercial SLD with a central wavelength of 1,285 nm and FWHM(Full Width at Half Maximum) of 35.3 nm. The optical delay line part is necessary to equal of the optical path length with scattered light or reflected light from sample. In order to equal the optical path length, the stage which is attached to reference mirror is moved linearly by step motor And the interferometer is configured with the Michelson interferometer using single mod fiber, the scanner can be focused of the sample by using the reference arm. Also, the 2-dimensional cross-sectional images were measured with scanning the transverse direction of the sample by using step motor. After detecting the internal signal of lateral direction at a paint of sample, scanner is moved to obtain the cross-sectional image of 2-demensional by using step motor. Photodiode has been used which has high detection sensitivity, excellent noise characteristic, and dynamic range from 800 nm to 1,700 nm. It is detected mixed small signal between noise and interference signal with high frequency After filtering and amplifying this signal, only envelope curve of interference signal is detected. And then, cross-sectional image is shown through converting this signal into digitalized signal using A／D converter. The resolution of the OCT system is about 30$\mu\textrm{m}$ which corresponds to the theoretical resolution. Also, the cross-sectional image of ping-pong ball is measured. The OCT system is configured with Michelson interferometer which has a low contrast because of reducing the power of feedback interference light. Such a problem is overcomed by using the improved inteferometer. Also, in order to obtain the cross-sectional image within a short time, it is necessary to reduce the measurement time for improving the optical delay line.

• Proceedings of the KOR-BRONCHOESO Conference
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• 1978.06a
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• pp.9.2-9
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• 1978

Monaural and binaural hearing aid performance under quiet and noisy conditions were compared in regard to (1) the degree of hearing impairment, (2) the symmetry of pure tone audiogram, (3) the automatic gain control of the hearing aid. (4) hearing impairement with recruitment and, word discrimination ability. Performance using binaural hearing aids was consistently superior to that using monaural hearing aids. The results were as follows. 1. Speech detection thresholds were enhanced by a mean of 4.25dB when tested with danavox 747 PP stereo type hearing aid and by a mean of 4.12 dB when tested hearing aids connected seperately to the right and left ears. 2. Binaurally tested speech reception thresholds were superior to monaurally tested thresholds by a mean of 3.56dB when tested in quiet and by a mean of 5.56dB when tested in noise. 3. Binaurally tested word discrimination scores were also superior by a mean of 17.09% in quiet and by a mean 19.63% in noise. 4. Both SRT and word discrimination scores were performed best by subjects with moderately-severe impairement. The performance by one mildly impaired subject was the poorest of all performances. The levels of performance order were; moderately-severe loss, severe loss. moderate loss and mild loss. 5. The data obtained using AGC aids when compaired with that of linear amplification show that when AGC aids were worn in both ears. the results were very poor but when one AGC aid was worn in one ear and linear amplification in the other. the results were good. 6. The advantages of binaural hearing aids were obvious even in cases 1) with great diferences in hearing thresholds between right and left ears, 2) when the subject was unable to discriminate words without vision and. 3) when the subject had extreme recruitme t phenomenon.

• Journal of the Korean Society of Radiology
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• v.82 no.6
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• pp.1477-1492
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• 2021

Purpose Dedicated breast CT is an emerging volumetric X-ray imaging modality for diagnosis that does not require any painful breast compression. To improve the detection rate of weakly enhanced lesions, an adaptive image rescaling (AIR) technique was proposed. Materials and Methods Two disks containing five identical holes and five holes of different diameters were scanned using 60/100 kVp to obtain single-energy CT (SECT), dual-energy CT (DECT), and AIR images. A piece of pork was also scanned as a subclinical trial. The image quality was evaluated using image contrast and contrast-to-noise ratio (CNR). The difference of imaging performances was confirmed using student's t test. Results Total mean image contrast of AIR (0.70) reached 74.5% of that of DECT (0.94) and was higher than that of SECT (0.22) by 318.2%. Total mean CNR of AIR (5.08) was 35.5% of that of SECT (14.30) and was higher than that of DECT (2.28) by 222.8%. A similar trend was observed in the subclinical study. Conclusion The results demonstrated superior image contrast of AIR over SECT, and its higher overall image quality compared to DECT with half the exposure. Therefore, AIR seems to have the potential to improve the detectability of lesions with dedicated breast CT.

• Maritime Security
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• v.7 no.1
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• pp.31-60
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• 2023

This study proposes a new convergence weapon system that combines the covert placement and detection abilities of a self-propelled mobile mine with the rapid tracking and attack abilities of supercavitating rocket torpedoes. This innovative system has been designed to counter North Korea's new underwater weapon, 'Haeil'. The concept behind this convergence weapon system is to maximize the strengths and minimize the weaknesses of each weapon type. Self-propelled mobile mines, typically placed discreetly on the seabed or in the water, are designed to explode when a vessel or submarine passes near them. They are generally used to defend or control specific areas, like traditional sea mines, and can effectively limit enemy movement and guide them in a desired direction. The advantage that self-propelled mines have over traditional sea mines is their ability to move independently, ensuring the survivability of the platform responsible for placing the sea mines. This allows the mines to be discreetly placed even deeper into enemy lines, significantly reducing the time and cost of mine placement while ensuring the safety of the deployed platforms. However, to cause substantial damage to a target, the mine needs to detonate when the target is very close - typically within a few yards. This makes the timing of the explosion crucial. On the other hand, supercavitating rocket torpedoes are capable of traveling at groundbreaking speeds, many times faster than conventional torpedoes. This rapid movement leaves little room for the target to evade, a significant advantage. However, this comes with notable drawbacks - short range, high noise levels, and guidance issues. The high noise levels and short range is a serious disadvantage that can expose the platform that launched the torpedo. This research proposes the use of a convergence weapon system that leverages the strengths of both weapons while compensating for their weaknesses. This strategy can overcome the limitations of traditional underwater kill-chains, offering swift and precise responses. By adapting the weapon acquisition criteria from the Defense force development Service Order, the effectiveness of the proposed system was independently analyzed and proven in terms of underwater defense sustainability, survivability, and cost-efficiency. Furthermore, the utility of this system was demonstrated through simulated scenarios, revealing its potential to play a critical role in future underwater kill-chain scenarios. However, realizing this system presents significant technical challenges and requires further research.

• 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.