• The Korean Journal of Nuclear Medicine Technology
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• v.21 no.2
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• pp.74-79
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• 2017

Purpose Proton therapy can deliver an optimal dose to tumor while reducing unnecessary dose to normal tissue as compared the conventional photon therapy. As proton beams are irradiated into tissue, various positron emitters are produced via nuclear fragmentation reactions. These positron emitters could be used for the dose verification by using PET. However, the short half-life of the radioisotopes makes it hard to obtain the enough amounts of events. The aim of this study is to investigate the effect of off-line PET imaging scan time on the PET image quality. Materials and Methods The various diameters of spheres (D=37, 28, 22 mm) filled with distilled water were inserted in a 2001 IEC body phantom. Then proton beams (100 MU) were irradiated into the center of the each sphere using the wobbling technique with the gantry angle of $0^{\circ}$. The modulation widths of the spread out bragg peak were 16.4, 14.7 and 9.3 cm for the spheres of 37, 28 and 22 mm in diameters respectively. After 5 min of the proton irradiation, the PET images of the IEC body phantom were obtained for 50 min. The PET images with different time courses (0-10 min, 11-20 min, 21-30 min, 31-40 min and 41-50 min) were obtained by dividing the frame with a duration of 10 min. In order to evaluate the off-line PET image quality with the different time courses, the contrast-to-noise ratio (CNR) of the PET image calculated for each sphere. Results The CNRs of the sphere (D=37 mm) were 0.43, 0.42, 0.40, 0.31 and 0.21 for the time courses of 0-10 min, 11-20 min, 21-30 min, 31-40 min and 41-50 min respectively. The CNRs of the sphere (D=28 mm) were 0.36, 0.32, 0.27, 0.19 and 0.09 for the time courses of 0-10 min, 11-20 min, 21-30 min, 31-40 min and 41-50 min respectively. The CNR of 37 mm sphere was decreased rapidly after 30 min of the proton irradiation. In case of the spheres of 28 mm and 22 mm, the CNR was decreased drastically after 20 min of the irradiation. Conclusion The off-line PET imaging time is an important factor for the monitoring of the proton therapy. In case of the lesion diameter of 22 mm, the off-line PET image should be obtained within 25 min after the proton irradiation. When it comes to small size of tumor, the long PET imaging time will be beneficial for the proton therapy treatment monitoring.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1042-1042
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from daily monitoring of two Japanese cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from two cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA md FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.5
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• pp.590-603
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• 2006

Size-and time-resolved aerosol samples were collected using an eight-stage Davis rotating unit for monitoring (DRUM) sampler from 23 March to 29 April 2001 at Gosan, Jeju Island, Korea, which is one of the super sites of Asia-Pacific Regional Aerosol Characterization Experiment(ACE-Asia). These samples were analyzed using synchrotron X-ray fluorescence for 3-hr average concentrations of 19 elements including Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Br, Rb, and Pb. The size-resolved data sets were then analyzed using the positive matrix factorization(PMF) technique to identify possible sources and estimate their contributions to particulate matter mass. PMF analysis uses the uncertainty of the measured data to provide an optimal weighting. Twelve sources were resolved in eight size ranges($0.09{\sim}12{\mu}m$) and included continental soil, local soil, sea salt, biomass/biofuel burning, coal combustion, oil combustion, municipal incineration, nonferrous metal source, ferrous metal source, gasoline vehicle, diesel vehicle, and volcanic emission. The PMF result of size-resolved source contributions showed that natural sources represented by local soil, sea salt, continental soil, and volcanic emission contributed about 79% to the predicted primary particulate matter(PM) mass in the coarse size range ($1.15{\sim}12{\mu}m$) while anthropogenic sources such as coal combustion and biomass/biofuel burning contributed about 58% in the fine size range($0.56{\sim}2.5{\mu}m$). The diesel vehicle source contributed mostly in ultra-fine size range($0.09{\sim}0.56{\mu}m$) and was responsible for about 56% of the primary PM mass.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.67-73
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• 2016

Recently, wireless power transmission has attracted much interest and is the subject of much research in industry and academia. As its name implies, it is a technology which involves transferring power without wires. This paper presents the design of an ICT-based wireless power transmission system. The proposed system consists of a wireless transceiver unit and high-efficiency coil unit, which can increase both the transmission efficiency and the effective power distance. In particular, the wireless transceiver unit was designed to work with the ICT technique to enable real-time remote monitoring. Also, studies were done relating to the effect of reducing the standby power. The optimal frequency of IGBT devices used in industrial wireless power systems of 20[KHz] was utilized. The values of $23.9[{\mu}H]$ and $2.64[{\mu}F]$ were selected for L and C, respectively, through many field experiments designed to optimize the system design. In addition, an output current controlling algorithm was developed for the purpose of reducing the standby power. The results presented in this paper represent a 75[%] to 85[%] higher power transmission efficiency with a 10[%] increase in the effective power transmission distance compared with the existing systems. As a result, the proposed system exhibits a lower standby power and maintenance costs. Also, the designed wireless transceiver unit facilitates fault detection by means of user acquired data with the development of the ICT applied program.

• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.121-133
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• 2021

Suspended Solids (SS) generated in rivers are mainly introduced from non-point pollutants or appear naturally in the water body, and are an important water quality factor that may cause long-term water pollution by being deposited. However, the conventional method of measuring the concentration of suspended solids is labor-intensive, and it is difficult to obtain a vast amount of data via point measurement. Therefore, in this study, a model for measuring the concentration of suspended solids based on remote sensing in the Nakdong River was developed using Sentinel-2 data that provides high-resolution multi-spectral satellite images. The proposed model considers the spectral bands and band ratios of various wavelength bands using a machine learning model, Support Vector Regression (SVR), to overcome the limitation of the existing remote sensing-based regression equations. The optimal combination of variables was derived using the Recursive Feature Elimination (RFE) and weight coefficients for each variable of SVR. The results show that the 705nm band belonging to the red-edge wavelength band was estimated as the most important spectral band, and the proposed SVR model produced the most accurate measurement compared with the previous regression equations. By using the RFE, the SVR model developed in this study reduces the variable dependence compared to the existing regression equations based on the single spectral band or band ratio and provides more accurate prediction of spatial distribution of suspended solids concentration.

• Journal of Cerebrovascular and Endovascular Neurosurgery
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• v.26 no.1
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• pp.1-10
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• 2024

Objective: Endovascular coil embolization is the primary treatment modality for intracranial aneurysms. However, its long-term durability remains of concern, with a considerable proportion of cases requiring aneurysm reopening and retreatment. Therefore, establishing optimal follow-up imaging protocols is necessary to ensure a durable occlusion. This study aimed to develop guidelines for follow-up imaging strategies after endovascular treatment of intracranial aneurysms. Methods: A committee comprising members of the Korean Neuroendovascular Society and other relevant societies was formed. A literature review and analyses of the major published guidelines were conducted to gather evidence. A panel of 40 experts convened to achieve a consensus on the recommendations using the modified Delphi method. Results: The panel members reached the following consensus: 1. Schedule the initial follow-up imaging within 3-6 months of treatment. 2. Noninvasive imaging modalities, such as three-dimensional time-of-flight magnetic resonance angiography (MRA) or contrast-enhanced MRA, are alternatives to digital subtraction angiography (DSA) during the first follow-up. 3. Schedule mid-term follow-up imaging at 1, 2, 4, and 6 years after the initial treatment. 4. If noninvasive imaging reveals unstable changes in the treated aneurysms, DSA should be considered. 5. Consider late-term follow-up imaging every 3-5 years for lifelong monitoring of patients with unstable changes or at high risk of recurrence. Conclusions: The guidelines aim to provide physicians with the information to make informed decisions and provide patients with high-quality care. However, owing to a lack of specific recommendations and scientific data, these guidelines are based on expert consensus and should be considered in conjunction with individual patient characteristics and circumstances.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.8
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• pp.367-376
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• 2007

In this paper, we introduce an application-specific and adaptive power management technique for portable systems that support dynamic voltage scaling (DVS). We exploit both the idle time of multitasking systems running soft real-time tasks as well as memory- or CPU-bound code regions. Detailed power and execution time profiles guide an adaptive power manager (APM) that is linked to the operating system. A post-pass optimizer marks candidate regions for DVS by inserting calls to the APM. At runtime, the APM monitors the CPU's performance counters to dynamically determine the affinity of the each marked region. for each region, the APM computes the optimal voltage and frequency setting in terms of energy consumption and switches the CPU to that setting during the execution of the region. Idle time is exploited by monitoring system idle time and switching to the energy-wise most economical setting without prolonging execution. We show that our method is most effective for periodic workloads such as video or audio decoding. We have implemented our method in a multitasking operating system (Microsoft Windows CE) running on an Intel XScale-processor. We achieved up to 9% of total system power savings over the standard power management policy that puts the CPU in a low Power mode during idle periods.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.235-243
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• 2023

Due to the revision of the River Act and the enactment of the Act on the Investigation, Planning, and Management of Water Resources, a regular bed change survey has become mandatory and a system is being prepared such that local governments can manage water resources in a planned manner. Since the topography of a bed cannot be measured directly, it is indirectly measured via contact-type depth measurements such as level survey or using an echo sounder, which features a low spatial resolution and does not allow continuous surveying owing to constraints in data acquisition. Therefore, a depth measurement method using remote sensing-LiDAR or hyperspectral imaging-has recently been developed, which allows a wider area survey than the contact-type method as it acquires hyperspectral images from a lightweight hyperspectral sensor mounted on a frequently operating drone and by applying the optimal bandwidth ratio search algorithm to estimate the depth. In the existing hyperspectral remote sensing technique, specific physical quantities are analyzed after matching the hyperspectral image acquired by the drone's path to the image of a surface unit. Previous studies focus primarily on the application of this technology to measure the bathymetry of sandy rivers, whereas bed materials are rarely evaluated. In this study, the existing hyperspectral image-based water depth estimation technique is applied to rivers with vegetation, whereas spatio-temporal hyperspectral imaging and cross-sectional hyperspectral imaging are performed for two cases in the same area before and after vegetation is removed. The result shows that the water depth estimation in the absence of vegetation is more accurate, and in the presence of vegetation, the water depth is estimated by recognizing the height of vegetation as the bottom. In addition, highly accurate water depth estimation is achieved not only in conventional cross-sectional hyperspectral imaging, but also in spatio-temporal hyperspectral imaging. As such, the possibility of monitoring bed fluctuations (water depth fluctuation) using spatio-temporal hyperspectral imaging is confirmed.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.

• Journal of Nutrition and Health
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• v.48 no.6
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• pp.542-557
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• 2015

Purpose: The aim of this study is to develop an evaluation tool for operation of food safety and nutrition education projects for middle class elderly using the concept of the balanced score card. Methods: After the draft of the evaluation tool for the elderly training projects was completed, it was revised into the questionnaire and the validity of the indicators was tested by the Delphi group. The validity of the indicators was rated using a 5-point scale. The Delphi group consisted of 26 experts in the education sector, 16 government officials, and 24 professionals of the related area in communities. The first round test was conducted from July 9 to July 17, 2012, and 45 persons responded. The second round test was conducted from July 18 to July 25 and 32 persons responded. Results: The indicators, which were answered by more than 75 percent of the experts as 'agree' (4 points), 'strongly agree' (5 point) were included as the final indicators for the evaluation tool: 28 items out of 36 in outcome perspectives, 9 items out of 12 in process perspectives, and 17 out of 20 items in structure perspectives. The score was allocated as 50 points for outcome indicators, 20 points for process indicators, and 30 points for structure indicators. Conclusion: Completion of the evaluation tool is a prerequisite to determine whether the program is effectively implemented. The monitoring tool developed in the study could be applied for identification of the most optimal delivery path for the food safety and nutrition education program, for the spread of the food safety and nutrition education program for middle class elderly.