• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.5
• /
• pp.11-19
• /
• 2014

In this paper, a low complexity parallel sphere decoder algorithm is proposed for high-order MIMO system. It reduces the computational complexity compared to the fixed-complexity sphere decoder (FSD) algorithm by static tree-pruning and dynamic tree-pruning using scalable node operators, and offers near-maximum likelihood decoding performance. Moreover, it also offers hardware-friendly node operation algorithm through fixing the variable computational complexity caused by the sequential nature of the conventional SD algorithm. A Monte Carlo simulation shows our proposed algorithm decreases the average number of expanded nodes by 55% with only 6.3% increase of the normalized decoding time compared to a full parallelized FSD algorithm for high-order MIMO communication system with 16 QAM modulation.

• International Journal of Fluid Machinery and Systems
• /
• v.8 no.2
• /
• pp.102-112
• /
• 2015

In the Nordic grid, a trend observed the recent years is the increase in grid frequency variations, which means the frequency is outside the normal range (49.9-50.1 Hz) more often. Variations in the grid frequency leads to changes in the speed of rotation of all the turbines connected to the grid, since the speed of rotation is closely related to the grid frequency for synchronous generators. When the speed of rotation changes, this implies that the net torque acting on the rotating masses are changed, and the material of the turbine runners must withstand these changes in torque. Frequency variations thus leads to torque oscillations in the turbine, which become dynamical loads that the runner must be able to withstand. Several new Francis runners have recently experienced cracks in the runner blades due to fatigue, obviously due to the runner design not taking into account the actual loads on the runner. In this paper, the torque oscillations and dynamic loads due to the variations in grid frequency are simulated in a 1D MATLAB program, and measured grid frequency is used as input to the simulation program. The maximum increase and decrease in the grid frequency over a 440 seconds interval have been investigated, in addition to an extreme event where the frequency decreased far below the normal range within a few seconds. The dynamic loading originating from grid frequency variations is qualitatively found by a constructed variable $T_{stress}$, and for the simulations presented here the variations in $T_{stress}$ are found to be around 3 % of the mean value, which is a relatively small dynamic load. The important thing to remember is that these dynamic loads come in addition to all other dynamic loads, like rotor-stator interaction and draft tube surges, and should be included in the design process, if not found to be negligible.

• Journal of Navigation and Port Research
• /
• v.44 no.2
• /
• pp.119-127
• /
• 2020

In this study, the current state of management efficiency of ocean carriers in Korea and the factors affecting them were analyzed. The purpose of this research is to enhance global competitiveness of ocean carriers by presenting suggestions that can improve management efficiency based on the analysis results. The measurement of management efficiency was made using the DEA model. The results of testing the adequacy of the input and output variables used are as follows. Appropriate inputs are total assets, cost of goods sold, charter expenses, sales and general management expenses, and interest expenses. Appropriate variables are sales, operating income, and operating cash flow. According to the analysis results of the DEA model by these variables, inefficient carriers (78%) are nearly four times more than efficient carriers(22%). However, container carriers have the most improved management efficiency compared to 2016 and 2017. According to the panel regression analysis, the charter rate has the greatest negative impact on efficiency (CRS), and the debt rate has a significant negative impact. Thus, it appears that reducing the charter size and the debt-to-sale rate facilitate improvement of the management efficiency of ocean carriers. Additionally, the pre-sales tax return rate, value added rate, total asset turnover rate, and the scale variable and interest coverage rate have a positive (+) effect. Thus ocean carriers should restore their global competitiveness by improving management efficiency by securing stable cargoes increasing sales profitability from the cost management perspective, increasing productivity, and enhancing the efficiency of their total assets through efficient fleet management.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.05a
• /
• pp.421-422
• /
• 2015

Deep learning, a deepen neural network technology that demonstrates the enhanced performance of neural network analysis, has been getting the spotlight in recent years. The present study proposed a process to test the error rates of certain variables and predict big data by using R, a analysis visualization tool based on deep learning, applying the RBM(Restricted Boltzmann Machine) algorithm to deep learning. The weighted value of each dependent variable was also applied after the classification of dependent variables. The investigator tested input data with the RBM algorithm and designed a process to detect error rates with the application of R.

• Journal of Environmental Science International
• /
• v.26 no.2
• /
• pp.221-230
• /
• 2017

To determine the effect of air pollution reduction policies, the long-term trend of air pollutants should be analyzed. Kolmogorov-Zurbenko (KZ) filter is a low-pass filter, produced through repeated iterations of a moving average to separate each variable into its temporal components. The moving average for a KZ(m, p) filter is calculated by a filter with window length m and p iterations. The output of the first pass subsequently becomes the input for the next pass. Adjusting the window length and the number of iterations makes it possible to control the filtering of different scales of motion. To break down the daily mean $PM_{10}$ into individual time components, we assume that the original time series comprises of a long-term trend, seasonal variation, and a short-term component. The short-term component is attributable to weather and short-term fluctuations in precursor emissions, while the seasonal component is a result of changes in the solar angle. The long-term trend results from changes in overall emissions, pollutant transport, climate, policy and/or economics. The long-term trend of the daily mean $PM_{10}$ decreased sharply from $59.6ug/m^3$ in 2002 to $44.6ug/m^3$ in 2015. This suggests that there was a long-term downward trend since 2005. The difference between the unadjusted and meteorologically adjusted long-term $PM_{10}$ is small. Therefore, we can conclude that $PM_{10}$ is unaffected by the meteorological variables (total insolation, daily mean temperature, daily mean relative humidity, daily mean wind speed, and daily mean local atmospheric pressure) in Busan.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.10 no.2
• /
• pp.91-102
• /
• 1990

The representative tests in this study are performed at a selected site which has the soil layers to analyze the safety and economy of the dynamic analysis for the variable soil conditions. Crosshole test and downhole test of small strain level tests and triaxial test of large strain level test are performed in the soil layers, and in the rock layers, crosshole test and downhole in-situ tests and laboratory sonic test are performed to measure the dynamic shear modulus, damping ratio, and Poisson$\acute{s}$ ratio of the soil and the rock. The correlations between the dynamic soil properties from the tests and the basic soil properties are determined through the regression analysis. The representative design value of the soil is determined by probability analysis of the test results. It is determined from the nonlinear stress-strain model in soils, and the value at small strain level is computed in rocks according to the distribution of the type of soils and the affecting variables. The constitutive value is systematized to be utilized in the analysis of the test results, and computation of the input soil data.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.38 no.1
• /
• pp.1-10
• /
• 2001

In order to generate the gray scale image by the binary state imaging devices such as a digital printer, the gray scale image needs to be converted into the binary image by the halftoning techniques. This paper presents a new error diffusion technique to achieve the homogeneous dot distributions on the binary images. In this paper,'the minimum pixel distance'from the current pixel under binarization to the nearest minor pixel is defined first. Also, the gray levels of the input image are converted into a new variable based on the principal distance for the error diffusion. In the proposed method, the difference in the principal distances is utilized for the error propagation, whereas the gray level difference due to the binarization is diffused to the neighboring pixels in the existing error diffusion techniques. The quantization is accomplished by comparing the updated principal distance with the minimum pixel distance. In order to calculate the minimum pixel distance, MPOA(Minor Pixel Offset Array) is employed to reduce the computational loads and memory resources.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.49 no.4
• /
• pp.42-53
• /
• 2012

In pattern recognition and machine learning society, classification has been a classical problem and the most widely researched area. Adaptive boosting also known as Adaboost has been successfully applied to binary classification problems. It is a kind of boosting algorithm capable of constructing a strong classifier through a weighted combination of weak classifiers. On the other hand, the PCA and LDA algorithms are the most popular linear feature extraction methods used mainly for dimensionality reduction. In this paper, the combination of Adaboost and feature extraction methods is proposed for efficient classification of two class data. Conventionally, in classification problems, the roles of feature extraction and classification have been distinct, i.e., a feature extraction method and a classifier are applied sequentially to classify input variable into several categories. In this paper, these two steps are combined into one resulting in a good classification performance. More specifically, each projection vector is treated as a weak classifier in Adaboost algorithm to constitute a strong classifier for binary classification problems. The proposed algorithm is applied to UCI dataset and FRGC dataset and showed better recognition rates than sequential application of feature extraction and classification methods.

• Journal of the Korean earth science society
• /
• v.28 no.2
• /
• pp.179-186
• /
• 2007

The response spectrum is one of the important basic materials for the aseismic design. Numerous strong ground motions based on the seismic source characteristics for the earthquakes occurring in the Korean Peninsula were simulated to obtain the response spectra by using the computer program, SMSIM, developed by Boore (2005). Through the extensive review of other study outcomes, the input data for the simulation such as seismic source and attenuation characteristics were selected. The spectra obtained from the simulated ground motions were normalized to 1.0 g of zero period acceleration and compared with the standard response spectrum proposed by the U.S. Atomic Energy Commission (AEC, 1973). In this study, we found that the spectral values for the response spectra appeared to be larger than those of the standard spectrum in the frequency band above roughly 10 Hz. The variation of resulting response spectra was evaluated with the variable stress drops. It was shown that the spectral amplitude of the spectrum for the larger stress drop denotes higher value in the low frequency range.

• Journal of Environmental Science International
• /
• v.26 no.12
• /
• pp.1307-1319
• /
• 2017

In this study, we investigated the impact of different initial data on atmospheric modeling results using the Weather Research and Forecast (WRF) model. Four WRF simulations were conducted with different initialization in March 2015, which showed the highest monthly mean $PM_{10}$ concentration in the recent ten years (2006-2015). The results of WRF simulations using NCEP-FNL and ERA-Interim were compared with observed surface temperature and wind speed data, and the difference of grid nudging effect on WRF simulation between the two data were also analyzed. The FNL simulation showed better accuracy in the simulated temperature and wind speed than the Interim simulation, and the difference was clear in the coastal area. The grid nudging effect on the Interim simulation was larger than that of the FNL simulation. Despite of the higher spatial resolution of ERA-Interim data compared to NCEP-FNL data, the Interim simulation showed slightly worse accuracy than those of the FNL simulation. It was due to uncertainties associated with the Sea Surface Temperature (SST) field in the ERA-Interim data. The results from the Interim simulation with different SST data showed significantly improved accuracy than the standard Interim simulation. It means that the SST field in the ERA-Interim data need to be optimized for the better WRF simulation. In conclusion, although the WRF simulation with ERA-Interim data does not show reasonable accuracy compared to those with NCEP-FNL data, it would be able to be Improved by optimizing the SST variable.