• Journal of Digital Convergence
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• v.20 no.4
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• pp.93-100
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• 2022

This study aimed to understand the current status of science and engineering university(SEU) R&D operations depending on the research project characteristics(e.g., stages and characteristics), then provide implications for future university R&D support systems and related policies. Hence, an online survey targeting SEU R&D recipients was conducted between October 4th to November 5th, 2021. Analyzing 445 valid data using the Apriori algorithm, 16 association rules for R&D operation according to the research project characteristics show that regardless of research characteristics, SEU's R&D projects, particularly in applied research, were funded or operated under the leadership of government or public institutions. For basic research, individual researchers had a higher level of autonomy in determining research topics; yet, they had a short duration (3 years) and a unit of evaluation period of more than 3 years. These findings can be empirical evidence for revealing the relationship among various variables in operating SEUs' R&D.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.8
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• pp.551-557
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• 2022

LRU (Line Replacement Unit) modifications are often required for military aircraft due to aging. Recently, LRU modifications were proceeded for KA-O (Armed Airborne Controller) by replacing the ejection seat and adding avionic equipment, which made the aircraft's operational CG (Center of Gravity) on fuel consumption curve become out of the range of the specification requested. The off-ranged CG should be corrected by introducing an appropriate method. This study proposes a procedure for revising and verifying the empty weight CG altered due to LRU modification for small military aircraft (e.g., KA-O). In the proposed method, first, the change of empty weight CG of KA-O due to the LRU modifications is comprehensively examined. Then, several ballast masses are added to the engine mount strut to restore the empty weight CG on the fuel consumption curve to a safe operational range. The installations are verified via stress and fatigue analysis for various operating conditions. Considering that open information is not very available for the revision of empty weight CG, this study is valuable because it presents an established procedure for correcting and verifying empty weight CG during aircraft modification.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.365-372
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• 2022

Compared to sensors mainly used in human activity recognition (HAR) systems, inertial measurement unit (IMU) sensors are small and light, so can achieve lightweight system at low cost. Therefore, in this paper, we propose a binary neural network (BNN) based gait pattern analysis system using IMU sensor, and present the design and implementation results of an FPGA-based accelerator for computational acceleration. Six signals for gait are measured through IMU sensor, and a spectrogram is extracted using a short-time Fourier transform. In order to have a lightweight system with high accuracy, a BNN-based structure was used for gait pattern classification. It is designed as a hardware accelerator structure using FPGA for computation acceleration of binary neural network. The proposed gait pattern analysis system was implemented using 24,158 logics, 14,669 registers, and 13.687 KB of block memory, and it was confirmed that the operation was completed within 1.5 ms at the maximum operating frequency of 62.35 MHz and real-time operation was possible.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.334-342
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• 2021

In Korea, the training is performed through independent environments without interoperability among L-V-C systems. In the L system, training for large units is limited due to civil complaints at the training grounds and road restrictions. The V system is insufficient in training related to tactical training, and the C system lacks practicality due to a lack of combat friction elements. To achieve synchronicity and integration training between upper and lower units, it is necessary to establish a system to ensure integrated training for each unit by interoperating the currently operating L, V, and C systems. The interoperability between the C-C system supports Korea-US Combined Exercise. On the other hand, the actual development of the training system through the interoperability of L, V, and C has not been made. Although efforts are being made to establish the L, V, and C system centering on the Army, the joint composite battlefield and LVC integrated architecture technology are not yet secured. Therefore, this paper proposes a new plan for the future training system by designing and implementing the LVC integrated architecture technology, which is the core technology that can build the L-V-C interoperability training system. In conclusion, a division-level L-V-C interoperability training system can be established in the future by securing the LVC integrated architecture technology.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.381-394
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• 2022

Recently, due to the personalization and customization of data, Internet-based services have increased requirements for real-time processing, such as real-time AI inference and data analysis, which must be handled immediately according to the user's situation or requirement. Real-time tasks have a set deadline from the start of each task to the return of the results, and the guarantee of the deadline is directly linked to the quality of the services. However, traditional container systems are limited in operating real-time tasks because they do not provide the ability to allocate and manage deadlines for tasks executed in containers. In addition, tasks such as AI inference and data analysis basically utilize graphical processing units (GPU), which typically have performance impacts on each other because performance isolation is not provided between containers. And the resource usage of the node alone cannot determine the deadline guarantee rate of each container or whether to deploy a new real-time container. In this paper, we propose a monitoring technique for tracking and managing the execution status of deadlines and real-time GPU tasks in containers to support real-time processing of GPU tasks running on containers, and a node list management technique for container placement on appropriate nodes to ensure deadlines. Furthermore, we demonstrate from experiments that the proposed technique has a very small impact on the system.

• Nutrition Research and Practice
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• v.16 no.5
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• pp.604-615
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• 2022

BACKGROUND/OBJECTIVES: This study aimed to investigate cancer-specific survival (CSS) and associated risk factors in elderly gastric cancer (EGC) patients. SUBJECTS/METHODS: EGC patients (≥ 70 yrs) who underwent curative gastrectomy between January 2013 and December 2017 at our hospital were included. Clinicopathologic characteristics and survival data were collected. Receiver operating characteristic (ROC) analysis was used to extract the best cutoff point for body mass index (BMI). A Cox proportional hazards model was used to determine the risk factors for CSS. RESULTS: In total, 290 EGC patients were included, with a median age of 74.7 yrs. The median follow-up time was 31 (1-77) mon. The postoperative 1-yr, 3-yr and 5-yr CSS rates were 93.7%, 75.9% and 65.1%, respectively. Univariate analysis revealed risk factors for CSS, including age (hazard ratio [HR] = 1.08; 95% confidence interval [CI], 1.01-1.15), intensive care unit (ICU) admission (HR = 1.73; 95% CI, 1.08-2.79), nutritional risk screening (NRS 2002) score ≥ 5 (HR = 2.33; 95% CI, 1.49-3.75), and preoperative prognostic nutrition index score < 45 (HR = 2.06; 95% CI, 1.27-3.33). The ROC curve showed that the best BMI cutoff value was 20.6 kg/m². Multivariate analysis indicated that a BMI ≤ 20.6 kg/m² (HR = 2.30; 95% CI, 1.36-3.87), ICU admission (HR = 1.97; 95% CI, 1.17-3.30) and TNM stage (stage II: HR = 5.56; 95% CI, 1.59-19.43; stage III: HR = 16.20; 95% CI, 4.99-52.59) were significantly associated with CSS. CONCLUSIONS: Low BMI (≤ 20.6 kg/m²), ICU admission and advanced pathological TNM stages (II and III) are independent risk factors for CSS in EGC patients after curative gastrectomy. Nutrition support, better perioperative management and early diagnosis would be helpful for better survival.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.103-112
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• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.10
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• pp.373-382
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• 2016

Under the Agricultural technology is influenced from climate that is requisite of seasonal. So this system will cover the problems and develop the agricultural industry as well. So far, the agricultural industry is developing however, it has the points of the weakness because of natural disasters such as wind risk and heavy snow. This paper designs system to change vinyl on the greenhouse. This is a preliminary study for the real-time feedback control of greenhouse. The study developed a wireless IoT sensor system based on authentic technology capacities, to integrate with the protected horticulture Management System. These system was used to evaluate the levels of the snow cover and wind through IoT devices. The existing greenhouse uses the warm water to clear snow or to change methods. This system will recover by changing the vinyl which is covered outside of the greenhouse. The points of the system is changing vinyl to spin pipe. It is contained extra vinyl. The effects of this system are minimized labor protected crops from natural disasters. For this purpose, the study first developed a wireless IoT sensor unit that integrates an MEMS device and wireless communication module. Also, the study developed an operating program that enables real-time response measurement. It will help operational and maintenance greenhouse as a result.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1185-1193
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• 2021

Objective: Clear cell sarcoma of the kidney (CCSK) is the second-most common but extremely rare primary renal malignancy in children after Wilms' tumor. The aims of this study were to evaluate the imaging features that could distinguish between CCSK and Wilms' tumor and to assess the features with diagnostic value for identifying CCSK. Materials and Methods: We reviewed the initial contrast-enhanced abdominal-pelvic CT scans of children with CCSK and Wilms' tumor between 2010 to 2019. Fifty-eight children (32 males and 26 females; age, 0.3-10 years), 7 with CCSK, and 51 with Wilms' tumor, were included. The maximum tumor diameter, presence of engorged perinephric vessels, maximum density of the tumor (Tmax) of the enhancing solid portion, paraspinal muscle, contralateral renal vein density, and density ratios (Tmax/muscle and Tmax/vein) were analyzed on the renal parenchymal phase of contrast-enhanced CT. Fisher's exact tests and Mann-Whitney U tests were conducted to analyze the categorical and continuous variables, respectively. Logistic regression and receiver operating characteristic curve analyses were also performed. Results: The age, sex, and tumor diameter did not differ between the two groups. Engorged perinephric vessels were more common in patients in the CCSK group (71% [5/7] vs. 16% [8/51], p = 0.005). Tmax (median, 148.0 vs. 111.0 Hounsfield unit, p = 0.004), Tmax/muscle (median, 2.64 vs. 1.67, p = 0.002), and Tmax/vein (median, 0.94 vs. 0.59, p = 0.002) were higher in the CCSK compared to the Wilms' group. Multiple logistic regression revealed that engorged vessels (odds ratio 13.615; 95% confidence interval [CI], 1.770-104.730) and Tmax/muscle (odds ratio 5.881; 95% CI, 1.337-25.871) were significant predictors of CCSK. The cutoff values of Tmax/muscle (86% sensitivity, 77% specificity) and Tmax/vein (71% sensitivity, 86% specificity) for the diagnosis of CCSK were 1.97 and 0.76, respectively. Conclusion: Perinephric vessel engorgement and greater tumor enhancement (Tmax/muscle > 1.97 or Tmax/vein > 0.76) are helpful for differentiating between CCSK and Wilms' tumor in children aged below 10 years.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.994-1004
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• 2021

Objective: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. Materials and Methods: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. Results: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79-0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77-0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85-0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66-0.88). Conclusion: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.