• Radiation Oncology Journal
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• v.31 no.4
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• pp.247-251
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• 2013

Purpose: This study aimed to investigate efficient approaches for determining internal target volume (ITV) from four-dimensional computed tomography (4D CT) images used in stereotactic body radiotherapy (SBRT) for patients with early-stage non-small cell lung cancer (NSCLC). Materials and Methods: 4D CT images were analyzed for 15 patients who received SBRT for stage I NSCLC. Three different ITVs were determined as follows: combining clinical target volume (CTV) from all 10 respiratory phases ($ITV_{10Phases}$); combining CTV from four respiratory phases, including two extreme phases (0% and 50%) plus two intermediate phases (20% and 70%) ($ITV_{4Phases}$); and combining CTV from two extreme phases ($ITV_{2Phases}$). The matching index (MI) of $ITV_{4Phases}$ and $ITV_{2Phases}$ was defined as the ratio of $ITV_{4Phases}$ and $ITV_{2Phases}$, respectively, to the $ITV_{10Phases}$. The tumor motion index (TMI) was defined as the ratio of $ITV_{10Phases}$ to $CTV_{mean}$, which was the mean of 10 CTVs delineated on 10 respiratory phases. Results: The ITVs were significantly different in the order of $ITV_{10Phases}$, $ITV_{4Phases}$, and $ITV_{2Phases}$ (all p < 0.05). The MI of $ITV_{4Phases}$ was significantly higher than that of $ITV_{2Phases}$ (p < 0.001). The MI of $ITV_{4Phases}$ was inversely related to TMI (r = -0.569, p = 0.034). In a subgroup with low TMI (n = 7), $ITV_{4Phases}$ was not statistically different from $ITV_{10Phases}$ (p = 0.192) and its MI was significantly higher than that of $ITV_{2Phases}$ (p = 0.016). Conclusion: The $ITV_{4Phases}$ may be an efficient approach alternative to optimal $ITV_{10Phases}$ in SBRT for early-stage NSCLC with less tumor motion.

• Journal of Korean Foot and Ankle Society
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• v.15 no.3
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• pp.124-131
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• 2011

Ankle arthrodesis has been considered to be the standard operative treatment for end-stage ankle arthritis, nevertheless currently increasing arthroplasty. Indication for arthrodesis is painful ankle from global arthrosis regardless of the etiology. But it is hard to be carried out in the several circumstance such as infection states, poor vascularity, severe diabetes, prematurity, etc. So thorough evaluation should be done before the surgery, including adjacent joints status. The ideal position for fusion is neutral in flexion, functional valgus, and slightly external rotation. Methods of arthrodesis would be largely divided into two categories as in situ fixation and realignment procedure. The lateral and anterior longitudinal approaches are two common procedures, and fixation modalities are also variable. The long-term results of arthrodesis have been reported. Even the close follow-up have shown subsequent degeneration of adjacent joints, benefits such as reliable pain loss, easy correctability for deformity, and improved functional status with considerable durability can be expected in the most patients.

• Journal of Radiation Protection and Research
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• v.45 no.2
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• pp.53-68
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• 2020

Background: International organizations such as the World Health Organization (WHO) and the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) reported public exposure doses due to radionuclides released in the Fukushima nuclear accident a few years after the event. However, the reported doses were generally overestimated due to conservative assumptions such as a longer stay in deliberate areas designated for evacuation than the actual stay. After these reports had been published, more realistic dose values were reported by Japanese scientists. Materials and Methods: The present paper reviews those reports, including the most recently published articles; and summarizes estimated effective doses (external and internal) and issues related to their estimation. Results and Discussion: External dose estimation can be categorized as taking two approaches-estimation from ambient dose rate and peoples' behavior patterns-and measurements using personal dosimeters. The former approach was useful for estimating external doses in an early stage after the accident. The first 4-month doses were less than 2 mSv for most (94%) study subjects. Later on, individual doses came to be monitored by personal dosimeter measurements. On the basis of these measurements, the estimated median annual external dose was reported to be < 1 mSv in 2011 for 22 municipalities of Fukushima Prefecture. Internal dose estimation also can be categorized as taking two approaches: estimation from whole-body counting and estimation from monitoring of environmental samples such as radioactivity concentrations in food and drinking water. According to results by the former approach, committed effective dose due to ¹³⁴Cs and ¹³⁷Cs could be less than 0.1 mSv for most residents including those from evacuated areas. Conclusion: Realistic doses estimated by Japanese scientists indicated that the doses reported by WHO and UNSCEAR were generally overestimated. Average values for the first-year effective doses for residents in two affected areas (Namie Town and Iitate Village) were not likely to reach 10 mSv, the lower end of the doses estimated by WHO.

• Genomics & Informatics
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• v.8 no.3
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• pp.150-158
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• 2010

Genome-wise association studies (GWASs) have become popular approaches to identify genetic variants associated with human biological traits. In this study, we applied Structural Equation Models (SEMs) in order to model complex relationships between genetic networks and traits as risk factors. SEMs allow us to achieve a better understanding of biological mechanisms through identifying greater numbers of genes and pathways that are associated with a set of traits and the relationship among them. For efficient SEM analysis for GWASs, we developed a procedure, comprised of four stages. In the first stage, we conducted single-SNP analysis using regression models, where age, sex, and recruited area were included as adjusting covariates. In the second stage, Fisher's combination test was conducted for each gene to detect significant genes using p-values obtained from the single-SNP analysis. In the third stage, Fisher's exact test was adopted to determine which biological pathways were enriched with significant SNPs. Finally, based on a pathway that was associated with the four traits in common, a SEM was fit to model a causal relationship among the genetic factors and traits. We applied our SEM model to GWAS data with four central obesity related traits: suprailiac and subscapular measures for upper body fat, BMI, and hypertension. Study subjects were collected from two Korean cohort regions. After quality control, 327,872 SNPs for 8842 individuals were included in the analysis. After comparing two SEMs, we concluded that suprailiac and subscapular measures may indirectly affect hypertension susceptibility by influencing BMI. In conclusion, our analysis demonstrates that SEMs provide a better understanding of biological mechanisms by identifying greater numbers of genes and pathways.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1003-1013
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• 2016

This paper proposes a framework of superpixel-based vehicle detection method using plane normal vector in disparity space. We utilize two common factors for detecting vehicles: Hypothesis Generation (HG) and Hypothesis Verification (HV). At the stage of HG, we set the regions of interest (ROI) by estimating the lane, and track them to reduce computational cost of the overall processes. The image is then divided into compact superpixels, each of which is viewed as a plane composed of the normal vector in disparity space. After that, the representative normal vector is computed at a superpixel-level, which alleviates the well-known problems of conventional color-based and depth-based approaches. Based on the assumption that the central-bottom of the input image is always on the navigable region, the road and obstacle candidates are simultaneously extracted by the plane normal vectors obtained from K-means algorithm. At the stage of HV, the separated obstacle candidates are verified by employing HOG and SVM as for a feature and classifying function, respectively. To achieve this, we trained SVM classifier by HOG features of KITTI training dataset. The experimental results demonstrate that the proposed vehicle detection system outperforms the conventional HOG-based methods qualitatively and quantitatively.

• Earthquakes and Structures
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• v.12 no.1
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• pp.55-65
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• 2017

Earthquake resilience of substations is essential for reliable and sustainable service of electrical grids. The majority of substation equipment consists of cylindrical porcelain components, which are vulnerable to earthquake shakings due to the brittleness of porcelain material. Failure of porcelain equipment has been repeatedly observed in recent earthquakes. Hence, proper seismic modelling of porcelain equipment is important for various limit state checks in both product manufacturing stage and detailed substation design stage. Sheds on porcelain core and cemented joint between porcelain component and metal cap have significant effects on the dynamic properties of the equipment, however, such effects have not been adequately parameterized in existing design guidelines. This paper addresses this critical issue by developing a method for taking these two effects into account in seismic modelling based on numerical and analytical approaches. Equations for estimating the effects of sheds and cemented joint on flexural stiffness are derived, respectively, by regression analyses based on the results of 12 pieces of full-scale equipment in 500kV class or higher. The proposed modelling technique has further been validated by shaking table tests.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.76-85
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• 2019

We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed forfull frame image. We reduced its computationalrequirementsignificantly without losing throughput and object detection accuracy with the proposed method.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3291-3300
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• 2023

Recently molybdenum alloys have been introduced as accident tolerating materials for cladding of fuel rods. Molybdenum element has seven stable isotopes with different neutron absorption cross section used in various fields, including nuclear physics and radioisotope production. This study presents separation approaches for all intermediate isotopes of molybdenum element by squared-off cascades using a newly developed numerical code with Salp Swarm Algorithm (SSA) optimization algorithm. The parameters of cascade including feed flow rate, feed entry stage, cascade cut, input feed flow rate to gas centrifuges (GCs), and cut of the first stage are optimized to maximize both isotope recovery and cascade capacity. The squared off and squared cascades are studied, and the efficiencies are compared. The results obtained from the optimization showed that for the selected squared off cascade, Mo⁹⁴ in four separation steps, Mo⁹⁵ in five steps, Mo⁹⁶ in six steps, Mo⁹⁷ in seven steps, and Mo⁹⁸ in two steps are separated to the desired concentrations. The highest recovery factor is obtained 63% for Mo⁹⁴ separation and lowest recovery factor is found 45% for Mo⁹⁵.

• Journal of Computing Science and Engineering
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• v.1 no.1
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• pp.56-73
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• 2007

Demands have been growing in safety-critical application fields for producing networked real-time embedded computing (NREC) systems together with acceptable assurances of tight service time bounds (STBs). Here a service time can be defined as the amount of time that the NREC system could take in accepting a request, executing an appropriate service method, and returning a valid result. Enabling systematic composition of large-scale NREC systems with STB certifications has been recognized as a highly desirable goal by the research community for many years. An appealing approach for pursuing such a goal is to establish a hard-real-time (HRT) component model that contains its own STB as an integral part. The TMO (Time-Triggered Message-Triggered Object) programming scheme is one HRT distributed computing (DC) component model established by the first co-author and his collaborators over the past 15 years. The TMO programming scheme has been intended to be an advanced high-level RT DC programming scheme that enables development of NREC systems and validation of tight STBs of such systems with efforts far smaller than those required when any existing lower-level RT DC programming scheme is used. An additional goal is to enable maximum exploitation of concurrency without damaging any major structuring and execution approaches adopted for meeting the first two goals. A number of previously untried program structuring approaches and execution rules were adopted from the early development stage of the TMO scheme. This paper presents new concrete justifications for those approaches and rules, and also discusses new extensions of the TMO scheme intended to enable further exploitation of concurrency in NREC system design and programming.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1037-1051
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• 2021

Biochemical oxygen demand (BOD) concentration, one of important water quality indicators, is treated as the measuring item for the ecological chapter in lakes and rivers. This investigation employed novel two-stage hybrid paradigm (i.e., wavelet-based gated recurrent unit, wavelet-based generalized regression neural networks, and wavelet-based random forests) to predict BOD concentration in the Dosan and Hwangji stations, South Korea. These models were assessed with the corresponding independent models (i.e., gated recurrent unit, generalized regression neural networks, and random forests). Diverse water quality and quantity indicators were implemented for developing independent and two-stage hybrid models based on several input combinations (i.e., Divisions 1-5). The addressed models were evaluated using three statistical indices including the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (CC). It can be found from results that the two-stage hybrid models cannot always enhance the predictive precision of independent models confidently. Results showed that the DWT-RF5 (RMSE = 0.108 mg/L) model provided more accurate prediction of BOD concentration compared to other optimal models in Dosan station, and the DWT-GRNN4 (RMSE = 0.132 mg/L) model was the best for predicting BOD concentration in Hwangji station, South Korea.