• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.3
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• pp.187-194
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• 2019

The off-axis Infra-Red(IR) seeker is mounted on the nose cone side of the anti-air high speed missile to alleviate thermal shield effect due to aerodynamic heating. The seeker output can not be regarded as the Line-of-Sight(LOS) rate any more as missile's roll motion to keep the target tracking is associated. In this paper, we propose a method to calculate the LOS rate for off-axis seeker on a 2-axis gimbal. Firstly, true LOS rate equations are analytically derived but not implementable because boresight error rate is not measurable. And then the first order lag approximation to obtain boresight error rate is proposed. The proposed LOS rate calculation method can compensate the coupling effect by considering the rotations of missile and gimbal. The performance of the proposed method is verified via full nonlinear 6-DOF(Degree of Freedom) simulations.

• Steel and Composite Structures
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• v.29 no.6
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• pp.785-802
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• 2018

In this paper, two different computational methods, called Rayleigh-Ritz and collocation are developed to estimate the ultimate strength of composite plates. Progressive damage behavior of moderately thick composite laminated plates is studied under in-plane compressive load and uniform lateral pressure. The formulations of both methods are based on the concept of the principle of minimum potential energy. First order shear deformation theory and the assumption of large deflections are used to develop the equilibrium equations of laminated plates. Therefore, Newton-Raphson technique will be used to solve the obtained system of nonlinear algebraic equations. In Rayleigh-Ritz method, two degradation models called complete and region degradation models are used to estimate the degradation zone around the failure location. In the second method, a new energy based collocation technique is introduced in which the domain of the plate is discretized into the Legendre-Gauss-Lobatto points. In this new method, in addition to the two previous models, the new model named node degradation model will also be used in which the material properties of the area just around the failed node are reduced. To predict the failure location, Hashin failure criteria have been used and the corresponding material properties of the failed zone are reduced instantaneously. Approximation of the displacement fields is performed by suitable harmonic functions in the Rayleigh-Ritz method and by Legendre basis functions (LBFs) in the second method. Finally, the results will be calculated and discussions will be conducted on the methods.

• Geomechanics and Engineering
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• v.31 no.1
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• pp.113-127
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• 2022

The undrained shear strength of soil is considered one of the engineering parameters of utmost significance in geotechnical design methods. In-situ experiments like cone penetration tests (CPT) have been used in the last several years to estimate the undrained shear strength depending on the characteristics of the soil. Nevertheless, the majority of these techniques rely on correlation presumptions, which may lead to uneven accuracy. This research's general aim is to extend a new united soft computing model, which is a combination of random forest (RF) with grasshopper optimization algorithm (GOA) to the pile set-up parameters' better approximation from CPT, based on two different types of data as inputs. Data type 1 contains pile parameters, and data type 2 consists of soil properties. The contribution of this article is that hybrid GOA - RF for the first time, was suggested to forecast the pile set-up parameter from CPT. In order to do this, CPT data and related bore log data were gathered from 70 various locations across Louisiana. With an R² greater than 0.9098, which denotes the permissible relationship between measured and anticipated values, the results demonstrated that both models perform well in forecasting the set-up parameter. It is comprehensible that, in the training and testing step, the model with data type 2 has finer capability than the model using data type 1, with R² and RMSE are 0.9272 and 0.0305 for the training step and 0.9182 and 0.0415 for the testing step. All in all, the models' results depict that the A parameter could be forecasted with adequate precision from the CPT data with the usage of hybrid GOA - RF models. However, the RF model with soil features as input parameters results in a finer commentary of pile set-up parameters.

• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.339-345
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• 2006

We suggest new 2D two-way Parabolic equation algorithm for multiple scattering. Our method is based on the successive performance of the single scattering approach. First. as the single scattering algorithm, the reflected and transmitted fields are calculated at the vertical interface of a range independent sector. Then. the reflected field is saved and the transmitted field Propagated to the next vertical interface with the split-step Pade method. After one step ends, the same Process is repeatedly performed with the change of the Propagation direction until the reflected field at the vertical interface is close to zero. Final incoming and outgoing fields are obtained as the sum of the wave fields obtained for each step. Our algorithm is relatively simple for the numerical implementation and requires less computational resources than the existing algorithm for multiple scattering

• Journal of Korean Neurosurgical Society
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• v.64 no.1
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• pp.125-135
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• 2021

Objective : This study aimed to validate the Korean version of the Repeatable Battery for the Assessment of Neuropsychological Status Update (K-RBANS). Methods : We performed a retrospective analysis of 283 psychiatric and neurosurgery patients. To investigate the convergent validity of the K-RBANS, correlation analyses were performed for other intelligence and neuropsychological test results. Confirmatory factor analysis was used to test a series of alternative plausible models of the K-RBANS. To analyze the various capabilities of the K-RBANS, we compared the area under the receiver operating characteristic (ROC) curves (AUC). Results : Significant correlations were observed, confirming the convergent validity of the K-RBANS among the Total Scale Index (TSI) and indices of the K-RBANS and indices of intelligence (r=0.47-0.81; p<0.001) and other neuropsychological tests at moderate and above significance (r=0.41-0.63; p<0.001). Additionally, the results testing the construct validity of the K-RBANS showed that the second-order factor structure model (model 2, similar to an original factor structure of RBANS), which includes a first-order factor comprising five index scores (immediate memory, visuospatial capacity, language, attention, delayed memory) and one higher-order factor (TSI), was statistically acceptable. The comparative fit index (CFI) (CFI, 0.949) values and the goodness of fit index (GFI) (GFI, 0.942) values higher than 0.90 indicated an excellent fit. The root mean squared error of approximation (RMSEA) (RMSEA, 0.082) was considered an acceptable fit. Additionally, the factor structure of model 2 was found to be better and more valid than the other model in χ2 values (Δχ2=7.69, p<0.05). In the ROC analysis, the AUCs of the TSI and five indices were 0.716-0.837, and the AUC of TSI (AUC, 0.837; 95% confidence interval, 0.760-0.896) was higher than the AUCs of the other indices. The sensitivity and specificity of TSI were 77.66% and 78.12%, respectively. Conclusion : The overall results of this study suggest that the K-RBANS may be used as a valid tool for the brief screening of neuropsychological patients in Korea.

• Journal of the Korean Magnetics Society
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• v.18 no.6
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• pp.211-216
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• 2008

The effects of Si impurity on electronic structures and magnetism of bcc Fe are investigated by using a first-principles method by considering spin-orbit coupling. In order to describe the Si impurity, a 27 atomic bcc Fe supercell has been considered. The Kohn-Sham equation was solved in terms of the all-electron full-potential linearized augmented plane wave (FLAPW) method within the generalized gradient approximation (GGA). The effects of spin-orbit coupling were calculated self-consistently by considering spin-diagonal terms based on second variation method. For the ferromagnetic (FM) state without considering SOC, the spin magnetic moment of the Si impurity was calculated to be $-0.143{\mu}B$, while the magnetic moments of Fe atoms were calculated to be $2.214{\mu}B$, $2.327{\mu}B$, and $2.354{\mu}B$ in away from the Si atom, respectively. However, the FM state with considering SOC, the spin magnetic moment of the Si impurity was calculated to be $-0.144{\mu}B$, which is not affected significantly by SOC, but the spin magnetic moments of Fe atoms were calculated $2.189{\mu}B$, $2.310{\mu}B$, and $2.325{\mu}B$, respectively, which are much reduced value compared to those of the FM state without SOC. Comparing the total charge density and spin density, those features are thought to be originated by the screening distortions of the Fe $t_{2g}$ orbital, which can be obtained by considering SOC.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2293-2302
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• 2013

Flood inundation map has been used as a fundamental information in flood risk management. However, there are various sources of uncertainty in flood inundation mapping, which can be another risk in preventing damage from flood. Therefore, it is necessary to remove or reduce uncertainty sources to improve the accuracy of flood inundation maps. However, the entire removal of uncertainty source may be impossible and inefficient due to limitations of knowledge and finance. Sensitivity analysis of uncertainty sources allows an efficient flood risk management by considering various conditions in flood inundation mapping because an uncertainty source under different conditions may propagate in different ways. The objectives of this study are (1) to perform sensitivity analysis of uncertainty sources by different conditions on flood inundation map using the FOA method and (2) to find a major contributor to a propagated uncertainty in the flood inundation map in Flatrock at Columbus, U.S.A. Result of this study illustrates that an uncertainty in a variable is differently propagated to flood inundation map by combination with other uncertainty sources. Moreover, elevation error was found to be the most sensitive to uncertainty in the flood inundation map of the study reach.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.620-626
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• 2004

For general nonlinear processes, it is difficult to control with a linear model-based control method and nonlinear controls are considered. Among the numerous approaches suggested, the most rigorous approach is to use dynamic optimization. Many general engineering problems like control, scheduling, planning etc. are expressed by functional optimization problem and most of them can be changed into dynamic programming (DP) problems. However the DP problems are used in just few cases because as the size of the problem grows, the dynamic programming approach is suffered from the burden of calculation which is called as 'curse of dimensionality'. In order to avoid this problem, the Neuro-Dynamic Programming (NDP) approach is proposed by Bertsekas and Tsitsiklis (1996). To get the solution of seriously nonlinear process control, the interest in NDP approach is enlarged and NDP algorithm is applied to diverse areas such as retailing, finance, inventory management, communication networks, etc. and it has been extended to chemical engineering parts. In the NDP approach, we select the optimal control input policy to minimize the value of cost which is calculated by the sum of current stage cost and future stages cost starting from the next state. The cost value is related with a weight square sum of error and input movement. During the calculation of optimal input policy, if the approximate cost function by using simulation data is utilized with Bellman iteration, the burden of calculation can be relieved and the curse of dimensionality problem of DP can be overcome. It is very important issue how to construct the cost-to-go function which has a good approximate performance. The neural network is one of the eager learning methods and it works as a global approximator to cost-to-go function. In this algorithm, the training of neural network is important and difficult part, and it gives significant effect on the performance of control. To avoid the difficulty in neural network training, the lazy learning method like k-nearest neighbor method can be exploited. The training is unnecessary for this method but requires more computation time and greater data storage. The pH neutralization process has long been taken as a representative benchmark problem of nonlin ar chemical process control due to its nonlinearity and time-varying nature. In this study, the NDP algorithm was applied to pH neutralization process. At first, the pH neutralization process control to use NDP algorithm was performed through simulations with various approximators. The global and local approximators are used for NDP calculation. After that, the verification of NDP in real system was made by pH neutralization experiment. The control results by NDP algorithm was compared with those by the PI controller which is traditionally used, in both simulations and experiments. From the comparison of results, the control by NDP algorithm showed faster and better control performance than PI controller. In addition to that, the control by NDP algorithm showed the good results when it applied to the cases with disturbances and multiple set point changes.

• Bulletin of the Korean Chemical Society
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• v.24 no.6
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• pp.864-880
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• 2003

A unified picture for magnetism, superconductivity, quantum optics and other properties of molecule-based materials has been presented on the basis of effective model Hamiltonians, where necessary parameter values have been determined by the first principle calculations of cluster models and/or band models. These properties of the matetials are qualitatively discussed on the basis of the spin and pseudo-spin Hamiltonian models, where several quantum operators are expressed by spin variables under the two level approximation. As an example, ab initio broken-symmetry DFT calculations are performed for cyclic magnetic ring constructed of 34 hydrogen atoms in order to obtain effective exchange integrals in the spin Hamiltonian model. The natural orbital analysis of the DFT solution was performed to obtain symmetry-adapted molecular orbitals and their occupation numbers. Several chemical indices such as information entropy and unpaired electron density were calculated on the basis of the occupation numbers to elucidate the spin and pair correlations, and bonding characteristic (kinetic correlation) of this mesoscopic magnetic ring. Both classical and quantum effects for spin alignments and singlet spin-pair formations are discussed on the basis of the true spin Hamiltonian model in detail. Quantum effects are also discussed in the case of superconductivity, atom optics and quantum optics based on the pseudo spin Hamiltonian models. The coherent and squeezed states of spins, atoms and quantum field are discussed to obtain a unified picture for correlation, coherence and decoherence in future materials. Implications of theoretical results are examined in relation to recent experiments on molecule-based materials and molecular design of future molecular soft materials in the intersection area between molecular and biomolecular materials.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.379-386
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• 2004

Video segmentation is a fundamental task in video indexing and it includes two kinds of shot change detections such as the abrupt transition and the gradual transition. The abrupt shot boundaries are detected by computing the image-based distance between adjacent frames and comparing this distance with a pre-determined threshold value. However, the gradual shot boundaries are difficult to detect with this approach. To overcome this difficulty, we propose the method that detects gradual transition in the MPEG compressed video using the HMM (Hidden Markov Model). We take two different HMMs such as a discrete HMM and a continuous HMM with a Gaussian mixture model. As image features for HMM's observations, we use two distinct features such as the difference of histogram of DC images between two adjacent frames and the difference of each individual macroblock's deviations at the corresponding macroblock's between two adjacent frames, where deviation means an arithmetic difference of each macroblock's DC value from the mean of DC values in the given frame. Furthermore, we obtain the DC sequences of P and B frame by the first order approximation for a fast and effective computation. Experiment results show that we obtain the best detection and classification performance of gradual transitions when a continuous HMM with one Gaussian model is taken and two image features are used together.