• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.565-573
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• 2017

This paper presents a multistage in-flight alignment (MIFA) method for a strapdown inertial navigation system (SDINS) suitable for moving vehicles with no initial attitude references. A SDINS mounted on a moving vehicle frequently loses attitude information for many reasons, and it makes solving navigation equations impossible because the true motion is coupled with an undefined vehicle attitude. To determine the attitude in such a situation, MIFA consists of three stages: a coarse horizontal attitude, coarse heading, and fine attitude with adaptive Kalman navigation filter (AKNF) in order. In the coarse horizontal alignment, the pitch and roll are coarsely estimated from the second order damping loop with an input of acceleration differences between the SDINS and GPS. To enhance estimation accuracy, the acceleration is smoothed by a scalar filter to reflect the true dynamics of a vehicle, and the effects of the scalar filter gains are analyzed. Then the coarse heading is determined from the GPS tracking angle and yaw increment of the SDINS. The attitude from these two stages is fed back to the initial values of the AKNF. To reduce the estimated bias errors of inertial sensors, special emphasis is given to the timing synchronization effects for the measurement of AKNF. With various real flight tests using an UH60 helicopter, it is proved that MIFA provides a dramatic position error improvement compared to the conventional gyro compass alignment.

• Nuclear Engineering and Technology
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• v.46 no.1
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• pp.73-80
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• 2014

A concrete cask is an option for spent nuclear fuel interim storage. A concrete cask usually consists of a metallic canister which confines the spent nuclear fuel assemblies and a concrete overpack. When the overpack undergoes a missile impact, which might be caused by a tornado or an aircraft crash, it should sustain an acceptable level of structural integrity so that its radiation shielding capability and the retrievability of the canister are maintained. A missile impact against a concrete overpack produces two damage modes, local damage and global damage. In conventional approaches [1], those two damage modes are decoupled and evaluated separately. The local damage of concrete is usually evaluated by empirical formulas, while the global damage is evaluated by finite element analysis. However, this decoupled approach may lead to a very conservative estimation of both damages. In this research, finite element analysis with material failure models and element erosion is applied to the evaluation of local and global damage of concrete overpacks under high speed missile impacts. Two types of concrete overpacks with different configurations are considered. The numerical simulation results are compared with test results, and it is shown that the finite element analysis predicts both local and global damage qualitatively well, but the quantitative accuracy of the results are highly dependent on the fine-tuning of material and failure parameters.

• Computers and Concrete
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• v.21 no.6
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• Current Optics and Photonics
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• v.2 no.2
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• pp.119-124
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• 2018

The errors in retrieved aerosol backscattering coefficients due to different lidar ratios are analyzed quantitatively in this paper. The actual calculation shows that the inversion error of the aerosol backscattering coefficients using the Fernald backward-integration method increases with increasing inversion distance. The greater the error in the lidar ratio, the faster the error in the aerosol backscattering coefficient increases. For the same error in lidar ratio, the smaller actual aerosol backscattering coefficient will get the larger relative error of the retrieved aerosol backscattering coefficient. The errors in the lidar ratios for dust or the cirrus layer have great impact on the retrievals of backscattering coefficients. The interval between the retrieved height and the reference range is one of the important factors for the derived error in the aerosol backscattering coefficient, which is revealed quantitatively for the first time in this paper. The conclusions of this article can provide a basis for error estimation in retrieved backscattering coefficients of background aerosols, dust and cirrus layer. The errors in the lidar ratio of an aerosol layer influence the retrievals of backscattering coefficients for the aerosol layer below it.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.102-107
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• 2019

This paper proposes a method to improve the mass spectral detection performance of the residual gas analyzer. By improving the mode estimation method for setting the threshold value and improving the additive noise elimination method, it is possible to detect mass spectrums having low peak values of the threshold level difficult to distinguish from noise. Ion signal blocks for each mass index with noise removed by the improved method are effective for eliminating invalid ion signals based on the linear and quadratic fittings. The mass spectrum can be obtained from the quadratic fitted curves for the reconstructed ion signal block using only the valid ion signals. In addition, the resolution of the mass spectrum can be improved by correcting the error caused by the shift of the spectral peak position. To verify the performance of the proposed method, computer simulations were performed using real ion signals obtained from the residual gas analysis system under development. The simulation results show that the proposed method is valid.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.2011-2018
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• 2021

Nowadays, evaluation of amounts and distributions of radioactive waste is an important preparatory step in the process of nuclear reactor decommissioning. For tentative estimation of radioactive waste, a cell-based rigorous 2 step (R2S) method usually is used; however, a poor resolution caused by the averaged flux and spectrum in a cell is still a great challenge because of leading to underestimated or overestimated results. To overcome the poor resolution, several systems were introduced. Neither system, however, provides any function for evaluation of radioactive waste amount and distribution. Thus, it is additionally required to classify radioactive waste based on the results of activation calculation. In this study, the advanced R2S (AR2S) system was developed. To verify the performance of the system, its results for a verification problem were compared with those of the cell-based R2S method. The results showed good agreement, which is to say, within 2.0% relative error. Also, several characteristics of fine/coarse mesh were analyzed. To demonstrate the performance of the AR2S system, the radioactive waste from the Japan Power Demonstration Reactor (JPDR) was estimated, and the result indicated a high-resolution distribution. Therefore, it is expected that the AR2S system will prove useful for precise evaluation of radioactive waste.

• Journal of the Korean Society of Visualization
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• v.20 no.2
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• pp.78-85
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• 2022

In these days, the rapid development in prediction technology using artificial intelligent is being applied in a variety of engineering fields. Especially, dimensionality reduction technologies such as autoencoder and convolutional neural network have enabled the classification and regression of high-dimensional data. In particular, pixel level prediction technology enables semantic segmentation (fine-grained classification), or physical value prediction for each pixel such as depth or surface normal estimation. In this study, the pressure distribution of the ship's surface was estimated at the pixel level based on the artificial neural network. First, a potential flow analysis was performed on the hull form data generated by transforming the baseline hull form data to construct 429 datasets for learning. Thereafter, a neural network with a U-shape structure was configured to learn the pressure value at the node position of the pretreated hull form. As a result, for the hull form included in training set, it was confirmed that the neural network can make a good prediction for pressure distribution. But in case of container ship, which is not included and have different characteristics, the network couldn't give a reasonable result.

• Journal of the Korean Geotechnical Society
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• v.25 no.2
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• pp.17-24
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• 2009

Undrained shear strength is estimated from laboratory tests generally, but the very soft or fluid material is generally incompatible with the test setup. In-situ methods require test to be accomplished at discrete time intervals, which does not provide a method to predict strength increment as a function of time for an ongoing project. Therefore, correlation between slump test value and undrained shear strength was derived through the regression analysis of slump test and laboratory vane shear test results. For the reliability of derived correlation equation statistical analysis using the t-distribution was performed and the comparison between the results of in-situ test and laboratory experiments demonstrated the applicability of the derived correlation.

• Geomechanics and Engineering
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• v.33 no.5
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• pp.487-506
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• 2023

Looking from the past to the present, the earthquakes can be said to be type of disaster with most casualties among natural disasters. Soil liquefaction, which occurs under repeated loads such as earthquakes, plays a major role in these casualties. In this study, analytical equation models were developed to predict the probability of occurrence of soil liquefaction. In this context, the parameters effective in liquefaction were determined out of 170 data sets taken from the real field conditions of past earthquakes, using WEKA decision tree. Linear, Exponential, Power and Quadratic models have been developed based on the identified earthquake and ground parameters using Krill Herd algorithm. The Exponential model, among the models including the magnitude of the earthquake, fine grain ratio, effective stress, standard penetration test impact number and maximum ground acceleration parameters, gave the most successful results in predicting the fields with and without the occurrence of liquefaction. This proposed model enables the researchers to predict the liquefaction potential of the soil in advance according to different earthquake scenarios. In this context, measures can be realized in regions with the high potential of liquefaction and these measures can significantly reduce the casualties in the event of a new earthquake.

• Journal of The Geomorphological Association of Korea
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• v.18 no.4
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• pp.163-175
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• 2011

Slackwater deposits are fine-grained flood sediments that deposited in areas of reduced velocity during flood period. These deposits have been used in numerous studies to estimate the magnitude and frequency of discrete flood events as the most commonly utilized PSIs (palaeostage indicators) in palaeoflood hydrology. Palaeoflood data by analysis of the slackwater deposits contribute to improve the estimation of flood-probability and reconstruct the palaeo-environment and past fluvial process. However, very few studies of these flood deposits have been carried out in Korea. Therefore, this study attempts to review the studies about slackwater deposits analysis and to investigate the characteristics, the research methods of slackwater deposits and the research-provability in Korea.