• The KSFM Journal of Fluid Machinery
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• v.15 no.5
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• pp.48-53
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• 2012

This paper presents a systematic procedure for three-dimensional noise analysis of an axial-flow fan by using computational aero-acoustics based on Ffowcs Williams-Hawkings equation. Flow-fields of a basic fan model are simulated by solving three-dimensional, unsteady, Reynolds-averaged Navier-Stokes equations using the commercial code ANSYS CFX 11.0. Starting with steady flow results, unsteady flow analysis is performed to extract the fluctuating pressures in the time domain at specified local points on the blade surface of the axial flow fan. The perturbed density wave by rotating blades reaches at the observer position, which is simulated by an in-house noise prediction software based on Ffowcs Williams-Hawkings equation. The detailed far-field noise signatures from the axial-flow fan are analyzed in terms of source types, field characteristics, and interpolation schemes.

• Korean Journal of Radiology
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• v.25 no.6
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• pp.550-558
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• 2024

Hepatocellular carcinoma (HCC) is a biologically heterogeneous tumor characterized by varying degrees of aggressiveness. The current treatment strategy for HCC is predominantly determined by the overall tumor burden, and does not address the diverse prognoses of patients with HCC owing to its heterogeneity. Therefore, the prognostication of HCC using imaging data is crucial for optimizing patient management. Although some radiologic features have been demonstrated to be indicative of the biologic behavior of HCC, traditional radiologic methods for HCC prognostication are based on visually-assessed prognostic findings, and are limited by subjectivity and inter-observer variability. Consequently, artificial intelligence has emerged as a promising method for image-based prognostication of HCC. Unlike traditional radiologic image analysis, artificial intelligence based on radiomics or deep learning utilizes numerous image-derived quantitative features, potentially offering an objective, detailed, and comprehensive analysis of the tumor phenotypes. Artificial intelligence, particularly radiomics has displayed potential in a variety of applications, including the prediction of microvascular invasion, recurrence risk after locoregional treatment, and response to systemic therapy. This review highlights the potential value of artificial intelligence in the prognostication of HCC as well as its limitations and future prospects.

• Investigative Magnetic Resonance Imaging
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• v.25 no.3
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• pp.164-171
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• 2021

Purpose: Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Brain atrophy in this disease spectrum begins in the medial temporal lobe structure, which can be recognized by magnetic resonance imaging. To overcome the unsatisfactory inter-observer reliability of visual evaluation, quantitative brain volumetry has been developed and widely investigated for the diagnosis of MCI and AD. The aim of this study was to assess the prediction accuracy of quantitative brain volumetry using a fully automated segmentation software package, NeuroQuant^®, for the diagnosis of MCI. Materials and Methods: A total of 418 subjects from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease cohort were included in our study. Each participant was allocated to either a cognitively normal old group (n = 285) or an MCI group (n = 133). Brain volumetric data were obtained from T1-weighted images using the NeuroQuant software package. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to investigate relevant brain regions and their prediction accuracies. Results: Multivariate logistic regression analysis revealed that normative percentiles of the hippocampus (P < 0.001), amygdala (P = 0.003), frontal lobe (P = 0.049), medial parietal lobe (P = 0.023), and third ventricle (P = 0.012) were independent predictive factors for MCI. In ROC analysis, normative percentiles of the hippocampus and amygdala showed fair accuracies in the diagnosis of MCI (area under the curve: 0.739 and 0.727, respectively). Conclusion: Normative percentiles of the hippocampus and amygdala provided by the fully automated segmentation software could be used for screening MCI with a reasonable post-processing time. This information might help us interpret structural MRI in patients with cognitive impairment.

• Archives of Craniofacial Surgery
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• v.19 no.2
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• pp.120-126
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• 2018

Background: Minimizing scarring has long been a challenge in plastic surgery. Factors affecting scar formation are well known, but the effect of some patient-specific factors such as dermal thickness remains unverified. Management of factors predictive of scarring can improve postoperative patient satisfaction and scar treatment. Methods: For 3 years, we used ultrasonography to measure dermal thickness in female patients who had undergone thyroidectomy for cancer at our hospital. We confirmed the influence of dermal thickness on hypertrophic scar formation and the Patient and Observer Scar Assessment Scale scar score 6 months after surgery. Results: There was a positive correlation between dermal thickness and scar score (p<0.05), and dermal thickness appears to be a cause of hypertrophic scar formation (p<0.05). Conclusion: Thick dermis was found to cause poor scar formation and hypertrophic scarring. Prediction of factors that can influence scar formation can be used to educate patients before surgery and can help in scar management and improvement in patient satisfaction.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.415-431
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• 2019

The satellite-viewed cloudiness, a ratio of cloudy pixels to total pixels ($C_{sat,\;prev}$), inevitably differs from the "ground-viewed" cloudiness ($C_{grd}$) due to different viewpoints. Here we develop an algorithm to retrieve the satellite-viewed, but adjusted cloudiness to $C_{grd} (C_{sat,\;adj})$. The key process of the algorithm is to convert the cloudiness projected on the plane surface into the cloudiness on the celestial hemisphere from the observer. For this conversion, the supplementary satellite retrievals such as cloud detection and cloud top pressure are used as they provide locations of cloudy pixels and cloud base height information, respectively. The algorithm is tested for Himawari-8 level 1B data. The $C_{sat,\;adj}$ and $C_{sat,\;prev}$ are retrieved and validated with $C_{grd}$ of SYNOP station over Korea (22 stations) and China (724 stations) during only daytime for the first seven days of every month from July 2016 to June 2017. As results, the mean error of $C_{sat,\;adj}$ (0.61) is less that than that of $C_{sat,\;prev}$ (1.01). The percent of detection for 'Cloudy' scenario of $C_{sat,\;adj}$ (73%) is higher than that of $C_{sat,\;prev}$ (60%) The percent of correction, the accuracy, of $C_{sat,\;adj}$ is 61%, while that of $C_{sat,\;prev}$ is 55% for all seasons. For the December-January-February period when cloudy pixels are readily overestimated, the proportion of correction of $C_{sat,\;adj$ is 60%, while that of $C_{sat,\;prev}$ is 56%. Therefore, we conclude that the present algorithm can effectively get the satellite cloudiness near to the ground-viewed cloudiness.

• Journal of Radiation Protection and Research
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• v.41 no.1
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• pp.41-47
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• 2016

Background: For the accurate dose assessment in radiation therapy, energy spectrum of the photon beam generated from the linac head is essential. The aim of this study is to develop the technique to accurately unfolding the energy spectrum with the transmission analysis method. Materials and Methods: Clinical linear accelerator and Monet Carlo method was employed to evaluate the transmission signals according to the thickness of the observer material, and then the response function of the ion chamber response was determined with the mono energy beam. Finally the energy spectrum was unfolded with HEPROW program. Elekta Synergy Flatform and Geant4 tool kits was used in this study. Results and Discussion: In the comparison between calculated and measured transmission signals using aluminum alloy as an attenuator, root mean squared error was 0.43%. In the comparison between unfolded spectrum using HEPROW program and calculated spectrum using Geant4, the difference of peak and mean energy were 0.066 and 0.03 MeV, respectively. However, for the accurate prediction of the energy spectrum, additional experiment with various type of material and improvement of the unfolding program is required. Conclusion: In this research, it is demonstrated that unfolding spectra technique could be used in megavoltage photon beam with aluminum alloy and HEPROW program.