• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.23-30
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• 2019

The purpose of this study is to find out the structure of the substance that affects antidiabetic using the candidate drug information for diabetes treatment. A quantitative structure activity relationship model based on machine learning method was constructed and major molecular descriptors were determined for each experimental data variables from coefficient values using a partial least squares algorithm. The results of the analysis of the molecular access system fingerprint data reflecting the candidate drug structure information were higher than those of the in vitro data analysis in terms of goodness-of-fit, and the major molecular expression factors affecting the antidiabetic effect were also variously derived. If the proposed method is applied to the new drug development environment, it is possible to reduce the cost for conducting candidate screening experiment and to shorten the search time for new drug development.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.3
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• pp.453-460
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• 2019

The SR-DCSK(Short Reference Differential Chaos Shift Keying) is a variant of DCSK that improves data transmission speed and energy efficiency without additional complexity. However, even when the reference signal of the optimum length is applied, the BER performance of the SR-DCSK is not better than that of the conventional DCSK. In this paper, we propose a scheme to improve the performance of SR-DCSK by applying two scale factors (scale coefficients) to the reference signal and the information signal, respectively. And the performance of the proposed method is analyzed by BER using Gaussian Approximation. Based on the derived BER expressions, we minimize the BER for a given system parameter to optimize the ratio of the two coefficients. Simulation results confirm that the BER of the proposed method is much improved over the SR-DCSK when we apply the optimal ratio of the two scale factors.

• Korean Journal of Exercise Nutrition
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• v.24 no.1
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• pp.9-13
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• 2020

[Purpose] This preliminary study aimed to develop a regression model to estimate the resting metabolic rate (RMR) of young and middle-aged Koreans using various easy-to-measure dependent variables. [Methods] The RMR and the dependent variables for its estimation (e.g. age, height, body mass index, fat-free mass; FFM, fat mass, % body fat, systolic blood pressure, diastolic blood pressure, mean arterial pressure, pulse pressure, and resting heart rate) were measured in 53 young (male n = 18, female n = 16) and middle-aged (male n = 5, female n = 14) healthy adults. Statistical analysis was performed to develop an RMR estimation regression model using the stepwise regression method. [Results] We confirmed that FFM and age were important variables in both the regression models based on the regression coefficients. Mean explanatory power of RMR₁ regression models estimated only by FFM was 66.7% (R²) and 66.0% (adjusted R²), while mean standard errors of estimates (SEE) was 219.85 kcal/day. Additionally, mean explanatory power of RMR₂ regression models developed by FFM and age were 70.0% (R²) and 68.8% (adjusted R²), while the mean SEE was 210.64 kcal/day. There was no significant difference between the measured RMR by the canopy method using a metabolic gas analyzer and the predicted RMR by RMR₁ and RMR₂ equations. [Conclusion] This preliminary study developed a regression model to estimate the RMR of young and middle-age healthy Koreans. The regression model was as follows: RMR₁ = 24.383 × FFM + 634.310, RMR₂ = 23.691 × FFM - 5.745 × age + 852.341.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1059-1068
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• 2022

A Post-Quantum Cryptographic algorithm NTRU, which is designed by considering the computational power of quantum computers, satisfies the mathematically security level. However, it should consider the characteristics of side-channel attacks such as power analysis attacks in hardware implementation. In this paper, we verify that the private key can be recovered by analyzing the power signal generated during the decryption process of NTRU. To recover the private keys, the Simple Power Analysis (SPA), Correlation Power Analysis (CPA) and Differential Deep Learning Analysis (DDLA) were all applicable. There is a shuffling technique as a basic countermeasure to counter such a power side-channel attack. Neverthe less, we propose a more effective method. The proposed method can prevent CPA and DDLA attacks by preventing leakage of power information for multiplication operations by only performing addition after accumulating each coefficient, rather than performing accumulation after multiplication for each index.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.11-18
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• 2023

This study explores a new energy partitioning approach to determine the fracture toughness of 3-D printed pristine/recycled high density polyethylene (HDPE) blends employing the essential work of fracture (EWF) concept. The traditional EWF approach conducts a uniaxial tensile test with double-edge notched tensile (DENT) specimens and measures the total energy defined by the area under a load-displacement curve until failure. The approach assumes that the entire total energy contributes to the fracture process only. This assumption is generally true for extruded polymers that fracture occurs in a material body. In contrast to the traditional extrusion manufacturing process, the current 3-D printing technique employs fused deposition modeling (FDM) that produces layer-by-layer structured specimens. This type of specimen tends to include separation energy even after the complete failure of specimens when the fracture test is conducted. The separation is not relevant to the fracture process, and the raw experimental data are likely to possess random variation or noise during fracture testing. Therefore, the current EWF approach may not be suitable for the fracture characterization of 3-D printed specimens. This paper proposed a new energy partitioning approach to exclude the irrelevant energy of the specimens caused by their intrinsic structural issues. The approach determined the energy partitioning location based on experimental data and observations. Results prove that the new approach provided more consistent results with a higher coefficient of correlation.

• Korean Chemical Engineering Research
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• v.61 no.3
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• pp.446-455
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• 2023

A mathematical model for predicting the liquid circulation velocity in an airlift reactor was developed based on the mechanical energy balance of the fluid circulation loop. The model considered the energy loss due to a 90° turn, the energy loss due to friction, and the energy loss due to the change in cross-sectional area at each part of the reactor. The model that separately considered the loss coefficients related to friction, direction change, and cross-sectional area change was able to predict the liquid circulation velocity better than the previous model using lumped parameters. The liquid circulation velocity was measured by the tracer pulse method. Most of our experimental results obtained in external-loop airlift reactors, which had the top and bottom connecting pipes, as well as other investigators' results obtained in various types of airlift reactors, were well predicted by the developed model with an error within 20%. Useful empirical equations for the loss coefficient related to the 90° turn of the circulating fluid were obtained in external and internal-loop airlift reactors and used to predict the liquid circulation velocity.

• Smart Structures and Systems
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• v.31 no.1
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• pp.1-11
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• 2023

Assessment of the compressive strength of concrete plays a major role during formwork removal and in the prestressing process. In concrete, temperature changes occur due to hydration which is an influencing factor that decides the compressive strength of concrete. Many methods are available to find the compressive strength of concrete, but the maturity method has the advantage of prognosticating strength without destruction. The temperature-time factor is found using a LM35 temperature sensor through the IoT technique. An experimental investigation was carried out with 56 concrete cubes, where 35 cubes were for obtaining the compressive strength of concrete using a universal testing machine while 21 concrete cubes monitored concrete's temperature by embedding a temperature sensor in each grade of M25, M30, M35, and M40 concrete. The mathematical prediction model equation was developed based on the temperature-time factor during the early age compressive strength on the 1^st, 2^nd, 3^rd and 7^th days in the M25, M30, M35, and M40 grades of concrete with their temperature. The 14^th, 21^st and 28^th day's compressive strength was predicted with the mathematical predicted equation and compared with conventional results which fall within a 2% difference. The compressive strength of concrete at any desired age (day) before reaching 28 days results in the discovery of the prediction coefficient. Comparative analysis of the results found by the predicted mathematical model show that, it was very close to the results of the conventional method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3A
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• pp.539-546
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• 2006

The reliability analysis of simply-supported composite plate girder and box girder bridges under positive flexure is performed. The bridges are designed based on the AASHTO-LRFD specification. A performance function for flexural failure is expressed as a function of such random variables as flexural resistance of composite section and design moments due to permanent load and live load. For the flexural resistance, the statistical parameters obtained by analyzing over 16,000 samples of domestic structural steel products are used. Several different values of statistical parameters with the bias factor in the range of 0.95-1.05 and the coefficient of variation in the range of 0.15-0.25 are used for the live-load moment. Due to the lack of available domestic measured data on the dead load moment, the same values of statistical properties used in the calibration of AASHTO-LRFD are applied. The reliability indices for the composite plate girder and box girder bridges with various span lengths are calculated by applying the Rackwitz-Fiessler technique.

• Genomics & Informatics
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• v.21 no.3
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• pp.40.1-40.11
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• 2023

Microbial community profiling using 16S rRNA amplicon sequencing allows for taxonomic characterization of diverse microorganisms. While amplicon sequence variant (ASV) methods are increasingly favored for their fine-grained resolution of sequence variants, they often discard substantial portions of sequencing reads during quality control, particularly in datasets with large number samples. We present a streamlined pipeline that integrates FastP for read trimming, HmmUFOtu for operational taxonomic units (OTU) clustering, Vsearch for chimera checking, and Kraken2 for taxonomic assignment. To assess the pipeline's performance, we reprocessed two published stool datasets of normal Korean populations: one with 890 and the other with 1,462 independent samples. In the first dataset, HmmUFOtu retained 93.2% of over 104 million read pairs after quality trimming, discarding chimeric or unclassifiable reads, while DADA2, a commonly used ASV method, retained only 44.6% of the reads. Nonetheless, both methods yielded qualitatively similar β-diversity plots. For the second dataset, HmmUFOtu retained 89.2% of read pairs, while DADA2 retained a mere 18.4% of the reads. HmmUFOtu, being a closed-reference clustering method, facilitates merging separately processed datasets, with shared OTUs between the two datasets exhibiting a correlation coefficient of 0.92 in total abundance (log scale). While the first two dimensions of the β-diversity plot exhibited a cohesive mixture of the two datasets, the third dimension revealed the presence of a batch effect. Our comparative evaluation of ASV and OTU methods within this streamlined pipeline provides valuable insights into their performance when processing large-scale microbial 16S rRNA amplicon sequencing data. The strengths of HmmUFOtu and its potential for dataset merging are highlighted.

• Korean Journal of Radiology
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• v.21 no.4
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• pp.450-461
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• 2020

Objective: We performed a meta-analysis to evaluate the agreement of cardiac computed tomography (CT) with cardiac magnetic resonance imaging (CMRI) in the assessment of right ventricle (RV) volume and functional parameters. Materials and Methods: PubMed, EMBASE, and Cochrane library were systematically searched for studies that compared CT with CMRI as the reference standard for measurement of the following RV parameters: end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), or ejection fraction (EF). Meta-analytic methods were utilized to determine the pooled weighted bias, limits of agreement (LOA), and correlation coefficient (r) between CT and CMRI. Heterogeneity was also assessed. Subgroup analyses were performed based on the probable factors affecting measurement of RV volume: CT contrast protocol, number of CT slices, CT reconstruction interval, CT volumetry, and segmentation methods. Results: A total of 766 patients from 20 studies were included. Pooled bias and LOA were 3.1 mL (-5.7 to 11.8 mL), 3.6 mL (-4.0 to 11.2 mL), -0.4 mL (5.7 to 5.0 mL), and -1.8% (-5.7 to 2.2%) for EDV, ESV, SV, and EF, respectively. Pooled correlation coefficients were very strong for the RV parameters (r = 0.87-0.93). Heterogeneity was observed in the studies (I² > 50%, p < 0.1). In the subgroup analysis, an RV-dedicated contrast protocol, ≥ 64 CT slices, CT volumetry with the Simpson's method, and inclusion of the papillary muscle and trabeculation had a lower pooled bias and narrower LOA. Conclusion: Cardiac CT accurately measures RV volume and function, with an acceptable range of bias and LOA and strong correlation with CMRI findings. The RV-dedicated CT contrast protocol, ≥ 64 CT slices, and use of the same CT volumetry method as CMRI can improve agreement with CMRI.