• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.99-107
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• 2024

Along with economic growth and industrial development, there is an increasing demand for various electronic components and device production of semiconductor, SMT component, and electrical battery products. However, these products may contain foreign substances coming from manufacturing process such as iron, aluminum, plastic and so on, which could lead to serious problems or malfunctioning of the product, and fire on the electric vehicle. To solve these problems, it is necessary to determine whether there are foreign materials inside the product, and may tests have been done by means of non-destructive testing methodology such as ultrasound ot X-ray. Nevertheless, there are technical challenges and limitation in acquiring X-ray images and determining the presence of foreign materials. In particular Small-sized or low-density foreign materials may not be visible even when X-ray equipment is used, and noise can also make it difficult to detect foreign objects. Moreover, in order to meet the manufacturing speed requirement, the x-ray acquisition time should be reduced, which can result in the very low signal- to-noise ratio(SNR) lowering the foreign material detection accuracy. Therefore, in this paper, we propose a five-step approach to overcome the limitations of low resolution, which make it challenging to detect foreign substances. Firstly, global contrast of X-ray images are increased through histogram stretching methodology. Second, to strengthen the high frequency signal and local contrast, we applied local contrast enhancement technique. Third, to improve the edge clearness, Unsharp masking is applied to enhance edges, making objects more visible. Forth, the super-resolution method of the Residual Dense Block (RDB) is used for noise reduction and image enhancement. Last, the Yolov5 algorithm is employed to train and detect foreign objects after learning. Using the proposed method in this study, experimental results show an improvement of more than 10% in performance metrics such as precision compared to low-density images.

• Korean Journal of Food Science and Technology
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• v.48 no.2
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• pp.133-141
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• 2016

The physicochemical properties, antioxidant capacities, and sensory optimization of taro (Colocasia esculenta) under different aging conditions were investigated to develop black taro. Black taro was processed in three steps (steaming: $95{\pm}3^{\circ}C$ for 1 h; aging: 85, 90, $95^{\circ}C$ for 20, 40, and 60 h; drying: $60^{\circ}C$ for 24 h) and ground into a powder for all experiments. Black taro showed an increased crude fiber content and browning index compared to raw taro. Calcium oxalate contents, reducing sugar contents, moisture contents, and lightness values were decreased during the processing of taro. Improvements in total polyphenol content and antioxidant activity (DPPH, ABTS, FRAP) were observed in the black taro samples aged at higher temperature. Response surface methodology was used for sensory optimization, and the optimum aging conditions with the highest acceptance values were found to be $88.73^{\circ}C$ for 39.50 h for taste, and $88.82^{\circ}C$ for 42.60 h for overall acceptance.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.3
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• pp.234-240
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• 2007

Purpose: In this study we propose a block-iterative method for reconstructing Compton scattered data. This study shows that the well-known expectation maximization (EM) approach along with its accelerated version based on the ordered subsets principle can be applied to the problem of image reconstruction for Compton camera. This study also compares several methods of constructing subsets for optimal performance of our algorithms. Materials and Methods: Three reconstruction algorithms were implemented; simple backprojection (SBP), EM, and ordered subset EM (OSEM). For OSEM, the projection data were grouped into subsets in a predefined order. Three different schemes for choosing nonoverlapping subsets were considered; scatter angle-based subsets, detector position-based subsets, and both scatter angle- and detector position-based subsets. EM and OSEM with 16 subsets were performed with 64 and 4 iterations, respectively. The performance of each algorithm was evaluated in terms of computation time and normalized mean-squared error. Results: Both EM and OSEM clearly outperformed SBP in all aspects of accuracy. The OSEM with 16 subsets and 4 iterations, which is equivalent to the standard EM with 64 iterations, was approximately 14 times faster in computation time than the standard EM. In OSEM, all of the three schemes for choosing subsets yielded similar results in computation time as well as normalized mean-squared error. Conclusion: Our results show that the OSEM algorithm, which have proven useful in emission tomography, can also be applied to the problem of image reconstruction for Compton camera. With properly chosen subset construction methods and moderate numbers of subsets, our OSEM algorithm significantly improves the computational efficiency while keeping the original quality of the standard EM reconstruction. The OSEM algorithm with scatter angle- and detector position-based subsets is most available.

• The Korean Journal of Nuclear Medicine
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• v.32 no.6
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• pp.490-496
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• 1998

Purpose: We evaluated brain perfusion SPECT findings of MELAS syndrome and mitochondrial myopathy in correlation with MR imaging in search of specific imaging features. Materials and Methods: Subjects were five patients (four females and one male; age range, 1 to 25 year) who presented with repeated stroke-like episodes, seizures or developmental delay or asymptomatic but had elevated lactic acid in CSF and serum. Conventional non-contrast MR imaging and Tc-99m-ethyl cysteinate dimer (ECD) brain perfusion SPECT were Performed and imaging features were analyzed. Results: MRI demonstrated increased T2 signal intensities in the affected areas of gray and white matters mainly in the parietal (4/5) and occipital lobes (4/5) and in the basal ganglia (1/5), which were not restricted to a specific vascular territory. SPECT demonstrated decreased perfusion in the corresponding regions of MRI lesions. In addition, there were perfusion defects in parietal (1 patient), temporal (2), and frontal (1) lobes and basal ganglia (1) and thalami (2). In a patient with mitochondrial myopathy who had normal MRI, decreased perfusion was noted in left parietal area and bilateral thalami. Conclusion: Tc-99m ECD SPECT imaging in patients with MELAS syndrome and mitochondrial myopathy showed hypoperfusion of parieto-occipital cortex, basal ganglia, thalamus and temporal cortex, which were not restricted to a specific vascular territory. There were no specific imaging features on SPECT. The significance of abnormal perfusion on SPECT without corresponding MR abnormalities needs to be evaluated further in larger number of patients.

• The Korean Journal of Nuclear Medicine
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• v.38 no.6
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• pp.486-491
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• 2004

Purpose: factor analysis and independent component analysis (ICA) has been used for handling dynamic image sequences. Theoretical advantages of a newly suggested ICA method, ensemble ICA, leaded us to consider applying this method to the analysis of dynamic myocardial $H_2^{15}O$ PET data. In this study, we quantified patients' blood flow using the ensemble ICA method. Materials and Methods: Twenty subjects underwent $H_2^{15}O$ PET scans using ECAT EXACT 47 scanner and myocardial perfusion SPECT using Vertex scanner. After transmission scanning, dynamic emission scans were initiated simultaneously with the injection of $555{\sim}740$ MBq $H_2^{15}O$. Hidden independent components can be extracted from the observed mixed data (PET image) by means of ICA algorithms. Ensemble learning is a variational Bayesian method that provides an analytical approximation to the parameter posterior using a tractable distribution. Variational approximation forms a lower bound on the ensemble likelihood and the maximization of the lower bound is achieved through minimizing the Kullback-Leibler divergence between the true posterior and the variational posterior. In this study, posterior pdf was approximated by a rectified Gaussian distribution to incorporate non-negativity constraint, which is suitable to dynamic images in nuclear medicine. Blood flow was measured in 9 regions - apex, four areas in mid wall, and four areas in base wall. Myocardial perfusion SPECT score and angiography results were compared with the regional blood flow. Results: Major cardiac components were separated successfully by the ensemble ICA method and blood flow could be estimated in 15 among 20 patients. Mean myocardial blood flow was $1.2{\pm}0.40$ ml/min/g in rest, $1.85{\pm}1.12$ ml/min/g in stress state. Blood flow values obtained by an operator in two different occasion were highly correlated (r=0.99). In myocardium component image, the image contrast between left ventricle and myocardium was 1:2.7 in average. Perfusion reserve was significantly different between the regions with and without stenosis detected by the coronary angiography (P<0.01). In 66 segment with stenosis confirmed by angiography, the segments with reversible perfusion decrease in perfusion SPECT showed lower perfusion reserve values in $H_2^{15}O$ PET. Conclusions: Myocardial blood flow could be estimated using an ICA method with ensemble learning. We suggest that the ensemble ICA incorporating non-negative constraint is a feasible method to handle dynamic image sequence obtained by the nuclear medicine techniques.

• The Korean Journal of Nuclear Medicine Technology
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• v.22 no.2
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• pp.67-73
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• 2018

$[^{11}C]PIB$ synthesis has been performed by a loop-methylation and HPLC purification in our lab. However, this method is time-consuming and requires complicated systems. Thus, we developed an on-cartridge method which simplified the synthetic procedure and reduced time greatly by removing HPLC purification step. We compared 6 different cartridges and evaluated the $[^{11}C]PIB$ production yields and specific activities. $[^{11}C]MeOTf$ was synthesized by using TRACERlab FXC Pro and was transferred into the cartridge by blowing with helium gas for 3 min. To remove byproducts and impurities, cartridges were washed out by 20 mL of 30% EtOH in 0.5 M $NaH_2PO_4$ solution (pH 5.1) and 10 mL of distilled water. And then, $[^{11}C]PIB$ was eluted by 5 mL of 30% EtOH in 0.5 M $NaH_2PO_4$ into the collecting vial containing 10 mL saline. Among the 6 cartridges, only tC18 environmental cartridge could remove impurities and byproducts from $[^{11}C]PIB$ completely and showed higher specific activity than traditional HPLC purification method. This method took only 8 ~ 9 min from methylation to formulation. For the tC18 environmental cartridge and conventional HPLC loop methods, the radiochemical yields were $12.3{\pm}2.2%$ and $13.9{\pm}4.4%$, respectively, and the molar activities were $420.6{\pm}20.4GBq/{\mu}mol$ (n=3) and $78.7{\pm}39.7GBq/{\mu}mol$ (n=41), respectively. We successfully developed a facile on-cartridge methylation method for $[^{11}C]PIB$ synthesis which enabled the procedure more simple and rapid, and showed higher molar radio-activity than HPLC purification method.

• The Journal of Korean Society for Radiation Therapy
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• v.32
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• pp.17-29
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• 2020

Purpose: The purpose of this work was to investigate the dosimetric impact of mixed energy partial arc technique on prostate cancer VMAT. Materials and Methods: This study involved prostate only patients planned with 70Gy in 30 fractions to the planning target volume (PTV). Femoral heads, Bladder and Rectum were considered as oragan at risk (OARs). For this study, mixed energy partial arcs (MEPA) were generated with gantry angle set to 180°~230°, 310°~50° for 6MV arc and 130°~50°, 310°~230° for 15MV arc. Each arc set the avoidance sector which is gantry angle 230°~310°, 50°~130° at first arc and 50°~310° at second arc. After that, two plans were summed and were analyzed the dosimetry parameter of each structure such as Maximum dose, Mean dose, D2%, Homogeneity index (HI) and Conformity Index (CI) for PTV and Maximum dose, Mean dose, V70Gy, V50Gy, V30Gy, and V20Gy for OARs and Monitor Unit (MU) with 6MV 1 ARC, 6MV, 10MV, 15MV 2 ARC plan. Results: In MEPA, the maximum dose, mean dose and D2% were lower than 6MV 1 ARC plan(p<0.0005). However, the average difference of maximum dose was 0.24%, 0.39%, 0.60% (p<0.450, 0.321, 0.139) higher than 6MV, 10MV, 15MV 2 ARC plan, respectively and D2% was 0.42%, 0.49%, 0.59% (p<0.073, 0.087, 0.033) higher than compared plans. The average difference of mean dose was 0.09% lower than 10MV 2 ARC plan, but it is 0.27%, 0.12% (p<0.184, 0.521) higher than 6MV 2 ARC, 15MV 2 ARC plan, respectively. HI was 0.064±0.006 which is the lowest value (p<0.005, 0.357, 0.273, 0.801) among the all plans. For CI, there was no significant differences which were 1.12±0.038 in MEPA, 1.12±0.036, 1.11±0.024, 1.11±0.030, 1.12±0.027 in 6MV 1 ARC, 6MV, 10MV, 15MV 2 ARC, respectively. MEPA produced significantly lower rectum dose. Especially, V70Gy, V50Gy, V30Gy, V20Gy were 3.40, 16.79, 37.86, 48.09 that were lower than other plans. For bladder dose, V30Gy, V20Gy were lower than other plans. However, the mean dose of both femoral head were 9.69±2.93, 9.88±2.5 which were 2.8Gy~3.28Gy higher than other plans. The mean MU of MEPA were 19.53% lower than 6MV 1 ARC, 5.7% lower than 10MV 2 ARC respectively. Conclusion: This study for prostate radiotherapy demonstrated that a choice of MEPA VMAT has the potential to minimize doses to OARs and improve homogeneity to PTV at the expense of a moderate increase in maximum and mean dose to the femoral heads.

• Journal of Food Hygiene and Safety
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• v.37 no.6
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• pp.373-384
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• 2022

Fluoxastrobin a fungicide developed from Strobilurus species mushroom extracts, can be used as an effective pesticide to control fungal diseases. In this study, we optimized the extraction and purification of fluoxastrobin according to its physical and chemical properties using the QuEChERS method and developed an LC-MS/MS-based analysis method. For extraction, we used acetonitrile as the extraction solvent, along with MgSO₄ and PSA. The limit of quantitation of fluoxastrobin was 0.01 mg/kg. We used 0.01, 0.1, and 0.5 mg/kg of five representative agricultural products and treated them with fluoxastrobin. The coefficients of determination (R²) of fluoxastrobin and fluoxastrobin Z isomer were > 0.998. The average recovery rates of fluoxastrobin (n=5) and fluoxastrobin Z isomer were 75.5-100.3% and 75.0-103.9%, respectively. The relative standard deviations (RSDs) were < 5.5% and < 4.3% for fluoxastrobin and fluoxastrobin Z isomer, respectively. We also performed an interlaboratory validation at Gwangju Regional Food and Drug Administration and compared the recovery rates and RSDs obtained for fluoxastrobin and fluoxastrobin Z isomer at the external lab with our results to validate our analysis method. In the external lab, the average recovery rates and RSDs of fluoxastrobin and fluoxastrobin Z isomer at each concentration were 79.5-100.5% and 78.8-104.7% and < 18.1% and < 10.2%, respectively. In all treatment groups, the concentrations were less than those described by the 'Codex Alimentarius Commission' and the 'Standard procedure for preparing test methods for food, etc.'. Therefore, fluoxastrobin is safe for use as a pesticide.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.