• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2002.11a
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• pp.59-62
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• 2002

Wavelength selection and prediction algorithm for determining hematocrit are investigated. A model based on the difference in optical density induced by the pulsation of heart beat is developed by taking approximation of Twersky's theory on the assumption that the variation of blood vessel size is small during arterial pulsing[1]. A device is constructed with a five-wavelength LED array as light source. The selected wavelengths are two isobestic points and three in compensation for tissue scattering. Data are collected from 549 out-patients who are randomly grouped as calibration and prediction sets. The range of percent hematocrit was 19.3∼51.8. The ratio of the variations of optical density between systole and diastole at two different wavelengths is used as a variable. We selected several such variables that show high reproducibility among all variables. Multiple linear regression analysis is made. The relative percent error is 8% and the standard deviation is 3.67 for the calibration set. The relative % error and standard deviation of the prediction set are 8.2% and 3.69 respectively. We successfully demonstrate the possibility of non-invasive hematocrit measurement, particularly, using the wavelengths below 1000nm.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.255-260
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• 2006

Safety-critical navigation systems have to provide 'reliable' position solutions, i.e., they must detect and exclude measurement or system faults and estimate the uncertainty of the solution. To obtain more accurate and reliable navigation systems, various filtering methods have been employed to reduce measurement noise level, or integrate sensors, such as global navigation satellite system/inertial navigation system (GNSS/INS) integration. Recently, particle filters have attracted attention, because they can deal with nonlinear/non-Gaussian systems. In most GNSS applications, the GNSS measurement noise is assumed to follow a Gaussian distribution, but this is not true. Therefore, we have proposed a fault detection and exclusion method using particle filters assuming non-Gaussian measurement noise. The performance of our method was contrasted with that of conventional Kalman filter methods with an assumed Gaussian noise. Since the Kalman filters presume that measurement noise follows a Gaussian distribution, they used an overbounded standard deviation to represent the measurement noise distribution, and since the overbound standard deviations were too conservative compared to the actual distributions, this degraded the integrity-monitoring performance of the filters. A simulation was performed to show the improvement in performance of our proposed particle filter method by not using the sigma overbounding. The results show that our method could detect smaller measurement biases and reduced the protection level by 30% versus the Kalman filter method based on an overbound sigma, which motivates us to use an actual noise model instead of the overbounding or improve the overbounding methods.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.900-907
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• 2000

The three-dimensional turbulent flow in curved pipes susceptible to flow-accelerated corrosion has been analyzed numerically to predict the pressure and shear stress distributions on the inner surface of the pipes. The analysis employs the body-fitted non-orthogonal curvilinear coordinate system and a standard $ {\kappa}-{\varepsilon}$ turbulence model with wall function method. The finite volume method is used to discretize the governing equations. The convection term is approximated by a high-resolution and bounded discretization scheme. The cell-centered, non-staggered grid arrangement is adopted and the resulting checkerboard pressure oscillation is prevented by the application of a modified version of momentum interpolation scheme. The SIMPLE algorithm is employed for the pressure and velocity coupling. The numerical calculations have been performed for two curved pipes with different bend angles and curvature radii, and discussions have been made on the distributions of the primary and secondary flow velocities, pressure and shear stress on the inner surface of the pipe to examine applicability of the present analysis method. As the result it is seen that the method is effective to predict the susceptible systems or their local areas where the fluid velocity or local turbulence is so high that the structural integrity can be threatened by wall thinning degradation due to flow-accelerated corrosion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1789-1798
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• 2012

IEEE 802.15.4a standard can provide a variety of location-based services for ZigBee or wireless network applications by adapting the time-of-arrival (TOA) ranging technique. The non-line-of-sight (NLOS) condition is the critical problem in the IEEE 802.15.4a networks, and it can significantly degrade the performance of the TOA-based localization. To enhance the location accuracy due to the NLOS problem, this paper proposes an energy-efficient low complexity localization algorithm. The proposed approach performs the ranging with the multicast method, which can reduce the message overhead due to packet exchanges. By limiting the search region for the location of the node, the proposed approach can enhance the location accuracy. Experimental results show that the proposed algorithm outperforms previous algorithms in terms of the energy consumption and the localization accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.5
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• pp.676-682
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• 2014

In this paper, precipitation echo(PRE) and non-precipitaion echo(N-PRE)(including ground echo and clear echo) through weather radar data are identified with the aid of neuro-fuzzy algorithm. The accuracy of the radar information is lowered because meteorological radar data is mixed with the PRE and N-PRE. So this problem is resolved by using RBFNN and judgement module. Structure expression of weather radar data are analyzed in order to classify PRE and N-PRE. Input variables such as Standard deviation of reflectivity(SDZ), Vertical gradient of reflectivity(VGZ), Spin change(SPN), Frequency(FR), cumulation reflectivity during 1 hour(1hDZ), and cumulation reflectivity during 2 hour(2hDZ) are made by using weather radar data and then each characteristic of input variable is analyzed. Input data is built up from the selected input variables among these input variables, which have a critical effect on the classification between PRE and N-PRE. Echo judgment module is developed to do echo classification between PRE and N-PRE by using testing dataset. Polynomial-based radial basis function neural networks(RBFNNs) are used as neuro-fuzzy algorithm, and the proposed neuro-fuzzy echo pattern classifier is designed by combining RBFNN with echo judgement module. Finally, the results of the proposed classifier are compared with both CZ and DZ, as well as QC data, and analyzed from the view point of output performance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.2
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• pp.111-120
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• 2017

This paper proposes an improved standard genetic algorithm (GA) of making a near optimal schedule for integrated process planning and scheduling problem (IPPS) considering tool flexibility and tool related constraints. Process planning involves the selection of operations and the allocation of resources. Scheduling, meanwhile, determines the sequence order in which operations are executed on each machine. Due to the high degree of complexity, traditionally, a sequential approach has been preferred, which determines process planning firstly and then performs scheduling independently based on the results. The two sub-problems, however, are complicatedly interrelated to each other, so the IPPS tend to solve the two problems simultaneously. Although many studies for IPPS have been conducted in the past, tool flexibility and capacity constraints are rarely considered. Various meta-heuristics, especially GA, have been applied for IPPS, but the performance is yet satisfactory. To improve solution quality against computation time in GA, we adopted three methods. First, we used a random circular queue during generation of an initial population. It can provide sufficient diversity of individuals at the beginning of GA. Second, we adopted an inferior selection to choose the parents for the crossover and mutation operations. It helps to maintain exploitation capability throughout the evolution process. Third, we employed a modification of the hybrid scheduling algorithm to decode the chromosome of the individual into a schedule, which can generate an active and non-delay schedule. The experimental results show that our proposed algorithm is superior to the current best evolutionary algorithms at most benchmark problems.

• Journal of The Geomorphological Association of Korea
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• v.25 no.3
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• pp.1-18
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• 2018

In Korea, 64% of the land is known as mountain area, but the definition and classification standard of mountain are not clear. Demand for utilization and development of mountain area is increasing. In this situation, the unclear definition and scope of the mountain area can lead to the destruction of the mountain and the increase of disasters due to indiscreet permission of forestland use conversion. Therefore, this study analyzed the variables and criteria that can extract the mountain boundaries through the questionnaire survey and the terrain analysis. We developed a mountain boundary extraction algorithm that can classify topographic mountain by using selected variables. As a result, 72.1% of the total land was analyzed as mountain area. For the three catchment areas with different mountain area ratio, we compared the results with the existing data such as forestland map and cadastral map. We confirmed the differences in boundary and distribution of mountain. In a catchment area with predominantly mountainous area, the algorithmbased mountain classification results were judged to be wider than the mountain or forest of the two maps. On the other hand, in the basin where the non-mountainous region predominated, algorithm-based results yielded a lower mountain area ratio than the other two maps. In the two maps, we was able to confirm the distribution of fragmented mountains. However, these areas were classified as non-mountain areas in algorithm-based results. We concluded that this result occurred because of the algorithm, so it is necessary to refine and elaborate the algorithm afterward. Nevertheless, this algorithm can analyze the topographic variables and the optimal value by watershed that can distinguish the mountain area. The results of this study are significant in that the mountain boundaries were extracted considering the characteristics of different mountain topography by region. This study will help establish policies for stable mountain management.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.577-582
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• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.4
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• pp.407-415
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• 2011

The stability condition and wideband characteristics of 3D ID-FDTD algorithm which has low dispersion error with isotropic dispersion are presented in this paper. 3D ID-FDTD method was proposed to improve the defect of the Yee FDTD such as the anisotropy and large dispersion error. The published paper calculated the stability condition of 3D ID-FDTD algorithm by using numerical method, however, it is thought that the examples were not sufficient to verify the stability condition. Thus, in this paper, various simulations are included in order to hold reliability under the conditions that the plane wave propagation is assumed with a single frequency and a wideband frequency. Also, the 3D ID-FDTD algorithm is compared to those that have the similar FDTD algorithm with ID-FDTD such as Forgy's method and non-standard FDTD method in a wideband. Finally, the radar cross section(RCS) for the large sphere with high dielectric constant is calculated.

• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.442-449
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• 2019

Objective : Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides more accurate data in spine osteoporosis. However, QCT has the disadvantage of additional radiation hazard and cost. The present study was to demonstrate the utility of artificial intelligence and machine learning algorithm for assessing osteoporosis using Hounsfield units (HU) of preoperative lumbar CT coupling with data of QCT. Methods : We reviewed 70 patients undergoing both QCT and conventional lumbar CT for spine surgery. The T-scores of 198 lumbar vertebra was assessed in QCT and the HU of vertebral body at the same level were measured in conventional CT by the picture archiving and communication system (PACS) system. A multiple regression algorithm was applied to predict the T-score using three independent variables (age, sex, and HU of vertebral body on conventional CT) coupling with T-score of QCT. Next, a logistic regression algorithm was applied to predict osteoporotic or non-osteoporotic vertebra. The Tensor flow and Python were used as the machine learning tools. The Tensor flow user interface developed in our institute was used for easy code generation. Results : The predictive model with multiple regression algorithm estimated similar T-scores with data of QCT. HU demonstrates the similar results as QCT without the discordance in only one non-osteoporotic vertebra that indicated osteoporosis. From the training set, the predictive model classified the lumbar vertebra into two groups (osteoporotic vs. non-osteoporotic spine) with 88.0% accuracy. In a test set of 40 vertebrae, classification accuracy was 92.5% when the learning rate was 0.0001 (precision, 0.939; recall, 0.969; F1 score, 0.954; area under the curve, 0.900). Conclusion : This study is a simple machine learning model applicable in the spine research field. The machine learning model can predict the T-score and osteoporotic vertebrae solely by measuring the HU of conventional CT, and this would help spine surgeons not to under-estimate the osteoporotic spine preoperatively. If applied to a bigger data set, we believe the predictive accuracy of our model will further increase. We propose that machine learning is an important modality of the medical research field.