Kim, Hyun-gi;Kim, Sungchan;Choi, Hyun-kyung;Hong, Seung-ho;Kim, Sang-Hyuck
Journal of Aerospace System Engineering
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v.16
no.1
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pp.97-103
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2022
An aircraft component can only be mounted on an aircraft if it has been certified to have a structural robustness under flight load conditions. Among the major components of the aircraft, a pylon is a structure that connects external equipment such as an engine, and external attachments with the main wing of an aircraft and transmits the loads acting on it to the main structure of the aircraft. In civil aircraft, when there is an incident of fire in the engine area, the pylon prevents the fire from spreading to the wings. This study presents the results of structural static tests performed to verify the structural robustness of a fuel pylon used to mount external fuel tank in an aircraft. In the main text, we present the test set-up diagram consisting of test fixture, hydraulic pressure unit, load control system, and data acquisition equipment used in the structure static test of the fuel pylon. In addition, we introduce the software that controls the load actuator, and provide a test profile for each test load condition. As a result of the structural static test, it was found that the load actuator was properly controlled within the allowable error range in each test, and the reliability of the numerical analysis was verified by comparing the numerical analysis results and the strain obtained from the structural test at the main positions of the test specimen. In conclusion, it was proved that the fuel pylon covered in this study has sufficient structural strength for the required load conditions through structural static tests.
Jae Beom Jeon;Cho Hee Lee;Yongwhan Lim;Min-Chul Kim;Hwa Jin Cho;Do Wan Kim;Kyo Seon Lee;In Seok Jeong
Journal of Chest Surgery
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v.56
no.4
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pp.244-251
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2023
Background: Extracorporeal membrane oxygenation (ECMO) has been widely used in patients with cardiorespiratory failure. The serum albumin level is an important prognostic marker in critically ill patients. We evaluated the efficacy of using pre-ECMO serum albumin levels to predict 30-day mortality in patients with cardiogenic shock (CS) who underwent venoarterial (VA) ECMO. Methods: We reviewed the medical records of 114 adult patients who underwent VA-ECMO between March 2021 and September 2022. The patients were divided into survivors and non-survivors. Clinical data before and during ECMO were compared. Results: Patients' mean age was 67.8±13.6 years, and 36 (31.6%) were female. The proportion of survival to discharge was 48.6% (n=56). Cox regression analysis showed that the pre-ECMO albumin level independently predicted 30-day mortality (hazard ratio, 0.25; 95% confidence interval [CI], 0.11-0.59; p=0.002). The area under the receiver operating characteristic curve of albumin levels (pre-ECMO) was 0.73 (standard error [SE], 0.05; 95% CI, 0.63-0.81; p<0.001; cut-off value=3.4 g/dL). Kaplan-Meier survival analysis showed that the cumulative 30-day mortality was significantly higher in patients with a pre-ECMO albumin level ≤3.4 g/dL than in those with a level >3.4 g/dL (68.9% vs. 23.8%, p<0.001). As the adjusted amount of albumin infused increased, the possibility of 30-day mortality also increased (coefficient=0.140; SE, 0.037; p<0.001). Conclusion: Hypoalbuminemia during ECMO was associated with higher mortality, even with higher amounts of albumin replacement, in patients with CS who underwent VA-ECMO. Further studies are needed to predict the timing of albumin replacement during ECMO.
KSCE Journal of Civil and Environmental Engineering Research
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v.28
no.6D
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pp.859-869
/
2008
These days, almost every industry's production line has become automatic and this phenomenon brought a lot of benefits such as increase in productivity and economical effect, assurance in industrial safety, better quality and compatibility. However, unlike industrial production line, in construction industry, automation has number of barriers like uncertainty incidents and intellectual judgment to make ability to make solution out of it. Therefore construction industry is still demanding use of construction machine through labor. Due to this matter operational labor in construction industry is aging and fading. To solve these problem, in developed nations like Europe, US or Japan are keep researching for the automation in construction and road pavement, strengthening and some other simple operations have been worked through automation but in civil engineering site, automation research is still low despite of its importance in constructional site. For automating civil engineering operation, effective operational plan have to be set by analyzing ground information acquainted. If skillful worker apply heuristics, trial & error can be reduced with increased safety and the effective work plan can be established. Hence, this research will introduce Intellectual Task Planning System for Intelligent Excavating System's effective work plan and heuristics applied in each steps.
Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.
Hyo Sang Lee;Yeongkuk Kim;Doo Ho Lee;Dongwon Seo;Dong Jae Lee;Chang Hee Do;Phuong Thanh N. Dinh;Waruni Ekanayake;Kil Hwan Lee;Duhak Yoon;Seung Hwan Lee;Yang Mo Koo
Journal of Animal Science and Technology
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v.65
no.4
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pp.720-734
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2023
In Korea, Korea Proven Bulls (KPN) program has been well-developed. Breeding and evaluation of cows are also an essential factor to increase earnings and genetic gain. This study aimed to evaluate the accuracy of cow breeding value by using three methods (pedigree index [PI], pedigree-based best linear unbiased prediction [PBLUP], and genomic-BLUP [GBLUP]). The reference population (n = 16,971) was used to estimate breeding values for 481 females as a test population. The accuracy of GBLUP was 0.63, 0.66, 0.62 and 0.63 for carcass weight (CWT), eye muscle area (EMA), back-fat thickness (BFT), and marbling score (MS), respectively. As for the PBLUP method, accuracy of prediction was 0.43 for CWT, 0.45 for EMA, 0.43 for MS, and 0.44 for BFT. Accuracy of PI method was the lowest (0.28 to 0.29 for carcass traits). The increase by approximate 20% in accuracy of GBLUP method than other methods could be because genomic information may explain Mendelian sampling error that pedigree information cannot detect. Bias can cause reducing accuracy of estimated breeding value (EBV) for selected animals. Regression coefficient between true breeding value (TBV) and GBLUP EBV, PBLUP EBV, and PI EBV were 0.78, 0.625, and 0.35, respectively for CWT. This showed that genomic EBV (GEBV) is less biased than PBLUP and PI EBV in this study. In addition, number of effective chromosome segments (Me) statistic that indicates the independent loci is one of the important factors affecting the accuracy of BLUP. The correlation between Me and the accuracy of GBLUP is related to the genetic relationship between reference and test population. The correlations between Me and accuracy were -0.74 in CWT, -0.75 in EMA, -0.73 in MS, and -0.75 in BF, which were strongly negative. These results proved that the estimation of genetic ability using genomic data is the most effective, and the smaller the Me, the higher the accuracy of EBV.
Bolus is used in radiation therapy to prescribe an even dose to the tumor when the skin surface is inclined or has irregularities. At this time, the dose to the skin surface increases. Due to the patient's unique body structure and irregular skin, voids may occur between the bolus and the skin, which may reduce the accuracy of treatment. Therefore, in this study, the existing bolus and the self-produced bolus through 3D printing were applied to the nasal area, and the difference between the surface dose after treatment plan and the dose directly measured with an Optically Stimulated luminescence(OSL) dosimeter was compared to the existing bolus. The bolus rate was 97%, PLA 100.33%, ePETELA 75A 100.53%, and ePETELA 85A 100.36%. It was confirmed that there was little error in the measurement values and treatment plan values for each material. In addition, compared to when applying a conventional bolus, a difference of -3% to +0.5% for a 3D printed bolus can be confirmed, so a customized bolus produced through 3D printing can complement the shortcomings of the existing bolus. It is believed that there will be.
International Journal of Computer Science & Network Security
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v.23
no.10
/
pp.1-10
/
2023
This paper proposes a method to extend Inter-Carrier Interference (ICI) canceling Orthogonal Frequency Division Multiplexing (OFDM) receivers for 5G mobile systems to spatial multiplexing 2×2 MIMO (Multiple Input Multiple Output) systems to support high-speed ground transportation services by linear motor cars traveling at 500 km/h. In Japan, linear-motor high-speed ground transportation service is scheduled to begin in 2027. To expand the coverage area of base stations, 5G mobile systems in high-speed moving trains will have multiple base station antennas transmitting the same downlink (DL) signal, forming an expanded cell size along the train rails. 5G terminals in a fast-moving train can cause the forward and backward antenna signals to be Doppler-shifted in opposite directions, so the receiver in the train may have trouble estimating the exact channel transfer function (CTF) for demodulation. A receiver in such high-speed train sees the transmission channel which is composed of multiple Doppler-shifted propagation paths. Then, a loss of sub-carrier orthogonality due to Doppler-spread channels causes ICI. The ICI Canceller is realized by the following three steps. First, using the Demodulation Reference Symbol (DMRS) pilot signals, it analyzes three parameters such as attenuation, relative delay, and Doppler-shift of each multi-path component. Secondly, based on the sets of three parameters, Channel Transfer Function (CTF) of sender sub-carrier number n to receiver sub-carrier number l is generated. In case of n≠l, the CTF corresponds to ICI factor. Thirdly, since ICI factor is obtained, by applying ICI reverse operation by Multi-Tap Equalizer, ICI canceling can be realized. ICI canceling performance has been simulated assuming severe channel condition such as 500 km/h, 8 path reverse Doppler Shift for QPSK, 16QAM, 64QAM and 256QAM modulations. In particular, 2×2MIMO QPSK and 16QAM modulation schemes, BER (Bit Error Rate) improvement was observed when the number of taps in the multi-tap equalizer was set to 31 or more taps, at a moving speed of 500 km/h and in an 8-pass reverse doppler shift environment.
The study primarily aims to examine the characteristics of the transition from natural landscape to modern agricultural landscape on the Daesan plain in Dong-myeon, Changwon-si, in the lower reaches of the Nakdong River. The periods covered in the transition include the late Joseon Dynasty, the early Japanese colonial period, and the late Japanese colonial period. The study concluded the following: It was found that the Daesan Plain used to function as a hydrophilic landscape before it formed into a rural landscape. This is characterized by the various water resources in the Plain, primarily by the Nakdong River, with its back marsh tributaries, the Junam Reservoir and Jucheon. To achieve its recent form, the Daesan Plain was subjected to human trial and error. Through installation of irrigation facilities such as embankments and sluices, the irregularly-shaped wetlands were transformed into large-scale farmlands while the same irrigation facilities underwent constant renovation to permanently stabilize the rural landscape. These processes of transformation were similarly a product of typical colonial expropriation. During the Japanese colonial period, Japanese capitalists initiated the construction of private farms which led to the national land development policy by the Governor-General of Korea. These landscape changes are indicative of resource capitalism depicted by the expansion of agricultural production value by the application of resource capital to undeveloped natural space for economic viability. As a result, the hierarchical structure was magnified resulting to the exacerbation of community and economic structural imbalances which presents an alternative yet related perspective to the evolution of landscapes during the Japanese colonial period. In addition, considering Daesan Plain's vulnerability to changing weather conditions, natural processes have also been a factor to its landscape transformation. Such occurrences endanger the sustainability of the area as when floods inundate cultivated lands and render them unstable, endangering residents, as well as the harvests. In conclusion, the Daesan Plain originally took the form of a hydrophilic landscape and started significantly evolving into a rural landscape since the Japanese colonial period. Human-induced land development and geophysical processes significantly impacted this transformation which also exemplifies the several ways of how undeveloped natural landscapes turn into mechanized and capitalized rural landscapes by colonial resource capitalism and development policies.
Journal of the Computational Structural Engineering Institute of Korea
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v.37
no.2
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pp.77-84
/
2024
In the fourth industrial-revolution era, the construction industry is transitioning from traditional methods to digital processes. This shift has been challenging owing to the industry's employment of diverse processes and extensive human resources, leading to a gradual adoption of digital technologies through trial and error. One critical area of focus is the safety management at construction sites, which is undergoing significant research and efforts towards digitization and automation. Despite these initiatives, recent statistics indicate a persistent occurrence of accidents and fatalities in construction sites. To address this issue, this study utilizes large-scale language-model artificial intelligence to analyze big data from a construction safety-management information network. The findings are integrated into on-site models, which incorporate real-time updates from detailed design models and are enriched with location information and spatial characteristics, for enhanced safety management. This research aims to develop a big-data-driven safety-management platform to bolster facility and worker safety by digitizing construction-site safety data. This platform can help prevent construction accidents and provide effective education for safety practices.
The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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v.29
no.1
/
pp.1-13
/
2024
The southwestern part of the East Sea is known to have a high primary productivity compared to those in the northern and eastern parts, which is attributed to nutrients supplies either by Tsushima Warm Current or by coastal upwelling. However, research on the biological pump in this area is limited. We developed machine learning models to estimate net community production (NCP), a measure of biological pump, with high spatial and time scales of 4 km and 8 days, respectively. The models were fed with the input parameters of sea surface temperature, chlorophyll-a, mixed layer depths, and photosynthetically active radiation and trained with observed NCP derived from high resolution measurements of surface O2/Ar. The root mean square error between the predicted values by the best performing machine model and the observed NCP was 6 mmol O2 m-2 d-1, corresponding to 15% of the average of observed NCP. The NCP in the central part of the Ulleung Basin was highest in March at 49 mmol O2 m-2 d-1 and lowest in June and July at 18 mmol O2 m-2 d-1. These seasonal variations were similar to the vertical nitrate flux based on the 3He gas exchange rate and to the particulate organic carbon flux estimated by the 234Th disequilibrium method. To expand this method, which produces NCP estimate for spring and summer, to autumn and winter, it is necessary to devise a way to correct bias in NCP by the entrainment of subsurface waters during the seasons.
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