• Advances in aircraft and spacecraft science
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• v.9 no.5
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• pp.377-400
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• 2022

The aim of this work is a numerical comparison (FEM) between lattice pyramidal-core panel and honeycomb core panel for different core thicknesses. By evaluating the mid-span deflection, the shear rigidity and the shear modulus for both core types and different core thicknesses, it is possible to define which core type has got the best mechanical behaviour for each thickness and the evolution of that behaviour as far as the thickness increases. Since a specific base geometry has been used for the lattice pyramidal core, the comparison gives us the opportunity to investigate the unit cell strut angle giving the higher mechanical properties. The presented work considers a detailed FEM modelling of a standard 3-point bending test (ASTM C393/C393M Standard Practice). Detailed FEM modelling addresses to detailed discretization of cores by means of beam elements for lattice core and shell elements for honeycomb core. Facings, instead, have been modelled by using shell elements for both sandwich panels. On lattice core structure, elements of core and facings are directly connected, to better simulate the additive manufacturing process. Otherwise, an MPC-based constraint between facings and core has been used for honeycomb core structure. Both sandwich panels are entirely built of Aluminium alloy. Prior to compare the two models, the FEM sandwich panel model with lattice pyramidal core needs to be validated with 3-point bending test experimental results, in order to ensure a good reliability of the FEM approach and of the comparison. Furthermore, the analytical validation has been performed according to Allen's theory. The FEM analysis is linear static with an increasing midspan load ranging from 50N up to 500N.

• Elastomers and Composites
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• v.59 no.1
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• pp.34-41
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• 2024

In this study, the effects of the type and content of silica on the mechanical and tear properties of chloroprene rubber (CR), which is mainly used as a jacket material for mining cables, were studied. The crosslinking density (ΔM) and reinforcing factor (α_f) defined using cure characteristics increased with increasing silica content, whereas the cure rate decreased. The hardness, tensile strength, and modulus of the CR compounds increased depending on the silica content and structural development. The reinforcing behavior of the silica-filled CR compounds according to the silica type and content showed the best fit with the Thomas equation of the predictive model. Tear strength was evaluated using two standard test methods, ASTM D470 and DIN VDE 0472-613, and the results were compared. The tear strength increased as the silica content increased, regardless of the test method, and the different tear strengths obtained by the two standard test methods showed a linear relationship with each other, indicating a high correlation.

• Journal of the Society of Naval Architects of Korea
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• v.35 no.2
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• pp.20-28
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• 1998

To accurately predict the motion of a submergible, the nonlinear structure of dynamic model should be selected and corresponding parameters should be estimated using model test. Providing the model structure, only the values of parameters are unknown and the estimation can thus be formulated as a standard least square problem. Unfortunately, the nonlinear model structure of submersibles is rarely known a prior and method of model structure determination from measurement data of model test should be developed and included as a vital part of the estimation procedure. In this study, the well-known linear least square algorithm for the analysis of model tests and a way to measure the goodness are reviewed, and the identification algorithm based on an orthogonal decomposition method of Gram-Schmidt is extended to combine structure selection and maneuvering coefficients estimation in a very simple and efficient manner. Finally, the efficiency of this algorithm is verified by using simulation and applying to the analysis of model test of a submerged body. As a result, it was verified that this combined method might be very erective in selecting the structure of dynamic model estimating the maneuvering coefficients from measurement fiat of model test.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.31 no.1
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• pp.121-126
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• 1998

This study is to examine the energy budget of Hexagrammos agrammus in the natural habitat, based on the von Bertalanffy's growth model using food consumption and growth data of the fish. The fish were collected at the coasts of Tongbaek Island in Pusan and Shinsu Island in Samchonpo, Korea. The standard energy budget model was adopted for this study and the model has the components of toed consumption (C), production (G), assimilation (A), absorption ($A_b$), catabolism (R), excreta (U) and feces (F). These components were expressed as mass unit, not as calorie unit as usual. Both the mass and the proportion of each component varied with age of the fish, The mass of annual excreta declined as the fish became older, while those of the other components increased with the age. The relationship between mean weight (W) and annual absorption ($A_b$) was a non-linear one with the equation of $A_b=4.592W^{0.666}$, while that between mean weight (W) and annual catabolism (R) was linear as R=0.007+0.567W. On the other hand, the annual food consumption (C) showed linear relations both with annual assimilation (A) and annual catabolism (R) as A= -7.026+0.061C and R=-20.749+0.048C, respectively.

• Tuberculosis and Respiratory Diseases
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• v.86 no.3
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• pp.203-215
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• 2023

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R² and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R² value was 0.27 and in set II, LightGBM was the best model with the highest R² value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R² value of 0.56 and the lowest MSE of 174.07. Conclusion: The LightGBM model showed the best performance in predicting postoperative lung function.

• Clinical and Experimental Reproductive Medicine
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• v.48 no.2
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• pp.163-173
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• 2021

Objective: This study aimed to characterize a validated model for predicting oocyte retrieval in controlled ovarian stimulation (COS) and to construct model-based nomograms for assistance in clinical decision-making regarding the gonadotropin protocol and dose. Methods: This observational, retrospective, cohort study included 636 women with primary unexplained infertility and a normal menstrual cycle who were attempting assisted reproductive therapy for the first time. The enrolled women were split into an index group (n=497) for model building and a validation group (n=139). The primary outcome was absolute oocyte count. The dose-response relationship was tested using modified Poisson, negative binomial, hybrid Poisson-E_max, and linear models. The validation group was similarly analyzed, and its results were compared to that of the index group. Results: The Poisson model with the log-link function demonstrated superior predictive performance and precision (Akaike information criterion, 2,704; λ=8.27; relative standard error (λ)=2.02%). The covariate analysis included women's age (p<0.001), antral follicle count (p<0.001), basal follicle-stimulating hormone level (p<0.001), gonadotropin dose (p=0.042), and protocol type (p=0.002 and p<0.001 for short and antagonist protocols, respectively). The estimates from 500 bootstrap samples were close to those of the original model. The validation group showed model assessment metrics comparable to the index model. Based on the fitted model, a static nomogram was built to improve visualization. In addition, a dynamic electronic tool was created for convenience of use. Conclusion: Based on our validated model, nomograms were constructed to help clinicians individualize the stimulation protocol and gonadotropin doses in COS cycles.

• Journal of the Korean Geotechnical Society
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• v.22 no.2
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• pp.41-53
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• 2006

The brittle materials like rocks show complicated strain-softening behavior after the peak which is hard to model using the classical constitutive models based on the relation between strain and stress tensors. A kinematically constrained three-dimensional microplane constitutive model is developed for granite. The model is verified by fitting the experimented data of Westerly granite and Bonnet granite. The triaxial behavior of granite is well reproduced by the model as well as the uniaxial behavior. We studied the development of the fracture zone in granite during blasting impact using the model with the standard finite element method. All the results obtained from the microplane model developed are compared to those from the linear elasticity model which is commonly used in many researches and practices. It is found that the nonlinearity of rocks sigificantly affects the results of analysis.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.535-540
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• 2006

The relationship between the observable noise model and the satellite elevation angle can be modeled quite well by an exponential function.[Jin, 1996] Noise size and dependence on the elevation angle are, however, different for each observation and receiver type. Therefore, the coefficient determination of this model is an issue, and various methods including PR-CP, single difference, and time difference have been suggested. The limitations of them are difficulty to model the carrier phase noise and to eliminate bias. To overcome these disadvantages for using Jin's model, we suggest zero baseline double difference (DD) and noise sorting algorithm. Data DD technique in zero baseline is useful to eliminate all the troublesome GPS biases, and the remaining error is the sum of GPS measurement noises from two satellites. These DD residuals for hours should be sorted by the combination of satellite elevation angles, and then variance value of the residual for each combination can be estimated. Using these values, we construct an over-determined linear equation whose solution is a set of noise variance for each satellite elevation angle. With 24hr Trimble 4000ssi data, we easily worked out the coefficients of the noise model not only for pseudorange but also for carrier phase. We estimated the standard deviation of the measurement DD using our model, and plotted 1 and 3 sigma lines for every epoch to verify the representation of the residual error. 63.3% of pseudorange residual and 65.9% of phase error did not exceed the 1 sigma lines. Additionally, 99.2% and 99.5% of them lied within 3sigma line. These figures prove that the Gaussian property of measurement noise, and that the suggested model by our algorithm corresponds to the observable noise information.

• Journal of Aerospace System Engineering
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• v.17 no.1
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• pp.79-87
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• 2023

To design a control law of an aircraft, an accurate aircraft dynamic model is required. To obtain an aerodynamic database (DB) to build a dynamic model, a large number of wind tunnel tests are typically required. However, when flight test data of target aircraft exist such as in the process of unmanned conversion of a manned aircraft, an aircraft dynamic model can be obtained through a parameter estimation method and a DB tuning procedure. This paper describes a nonlinear model construction process and a verification method for KC-100 OPV aircraft. Flight data compatibility analysis was performed to determine suitability of the estimation method application. Linear model estimation was performed using the maximum likelihood estimation method. Results of aerodynamic DB tuning process and verification applying the FFS standard to the nonlinear model constructed are presented.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.631-635
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• 2004

The rapid development of digital media and communication network urgently brings about the need of data certification technology to protect IPR (Intellectual property right). This paper proposed a new watermarking method for embedding contents owner's audio signal in order to protect color image IPR. Since this method evolves the existing static model and embeds audio signal of big data, it has the advantage of restoring signal transformed due to attacks. Three basic stages of watermarking include: 1) Encode analogue ID owner's audio signal using PCM and create new 3D audio watermark; 2) Interleave 3D audio watermark by linear bit expansion and 3) Transform Y signal of color image into wavelet and embed interleaved audio watermark in the low frequency band on the transform domain. The results demonstrated that the audio signal embedding in color image proposed in this paper enhanced robustness against lossy JPEG compression, standard image compression and image cropping and rotation which remove a part of image.