• Journal of Navigation and Port Research
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• v.39 no.1
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• pp.15-21
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• 2015

In high precision positioning systems based on GNSS, ambiguity resolution is an important procedure. Correct ambiguity leads to positioning results which have high precision between millimeters and centimeters. However, when the ambiguity is determined incorrectly, ensuring accuracy and precision of the positioning result is impossible. An ambiguity validation test is required to obtain correct ambiguity when ambiguity resolution is performed based on the ILS (Integer Least Squares), which shows the best performance in point of theory and experiment when compared with other methods such as IR (Integer Rounding) and IB (Integer Bootstrapping). Comparison between the candidates of the validation test is needed to judge ambiguity correctly, because ILS searches for candidates of integer ambiguity, unlike other methods which calculate only one integer ambiguity. We analyzed the experimental performance of ambiguity validation tests. R-ratio, F-ratio and W-ratio were adopted for analysis. The performance of validation tests was evaluated by classifying normal operation, detection, missed detection and false alarm. As a result, strengths and weaknesses of validation tests was showed to experimental. we concluded that validation tests must be selected according to environment.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.3
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• pp.127-138
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• 2016

Diesel vehicles should be equipped with urea-selective catalytic reduction(SCR) system as a high-performance catalyst, in order to reduce harmful nitrogen oxide emissions. In this study, a three-dimensional Eulerian-Lagrangian CFD analysis was used to numerically predict the multiphase flow characteristics of the urea-SCR system, coupled with the chemical reactions of the system's transport phenomena. Then, the numerical spray structure was modified by comparing the results with the measured values from spray visualization, such as the injection velocity, penentration length, spray radius, and sauter mean diameter. In addition, the analysis results were verified by comparison with the removal efficiency of the nitrogen oxide emissions during engine and chassis tests, resulting in accuracy of the relative error of less than 5%. Finally, a verified CFD analysis was used to calculate the interanl flow of the urea-SCR system, thereby analyzing the characteristics of pressure drop and velocity increase, and predicting the uniformity index and overdistribution positions of ammonia.

• Journal of Korea Water Resources Association
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• v.41 no.8
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• pp.855-862
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• 2008

Surface Image Velocimetry (SIV) is an instrument to measure water surface velocity by using image processing techniques. To improve its measuring accuracy, it is essential to get high quality images with low skewness. A truck-mounted SIV system would be a good way to get images, since its crane gives high altitude to the images. However, the images taken with a truck-mounted SIV would be swayed due to the movement of crane and the camera by winds. In that case, to analyze the images, it is necessary to compensate the side sway in the images. The present study is to develop an algorithm to analyze the swayed images by combining common image processing techniques and coordinate transform techniques. The system follows the traces of some selected fixed points and calculates the displacements of the video camera. By subtracting the average velocity of the fixed points from that of grid points, the velocity fields of the flow can be corrected. To evaluate the system's performance, two image sets were used, one image set without side sway and another set with side sway. The comparison of their results showed very close with the error of around 6 %.

• The KIPS Transactions:PartA
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• v.13A no.5 s.102
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• pp.465-472
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• 2006

Recently, many suggestions have been made in image segmentation methods for extracting human organs or disease affected area from huge amounts of medical image datasets. However, images from some areas, such as brain, which have multiple structures with ambiruous structural borders, have limitations in their structural segmentation. To address this problem, clustering technique which classifies voxels into finite number of clusters is often employed. This, however, has its drawback, the influence from noise, which is caused from voxel by voxel operations. Therefore, applying image enhancing method to minimize the influence from noise and to make clearer image borders would allow more robust structural segmentation. This research proposes an efficient structural segmentation method by filtering based clustering to extract detail structures such as white matter, gray matter and cerebrospinal fluid from brain MR. First, coherence enhancing diffusion filtering is adopted to make clearer borders between structures and to reduce the noises in them. To the enhanced images from this process, fuzzy c-means clustering method was applied, conducting structural segmentation by assigning corresponding cluster index to the structure containing each voxel. The suggested structural segmentation method, in comparison with existing ones with clustering using Gaussian or general anisotropic diffusion filtering, showed enhanced accuracy which was determined by how much it agreed with the manual segmentation results. Moreover, by suggesting fine segmentation method on the border area with reproducible results and minimized manual task, it provides efficient diagnostic support for morphological abnormalities in brain.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.4
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• pp.137-144
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• 2020

Delirium is among the most common mental disorders encountered in patients with a temporary cognitive impairment such as consciousness disorder, attention disorder, and poor speech, particularly among those who are older. Delirium is distressing for patients and families, can interfere with the management of symptoms such as pain, and is associated with increased elderly mortality. The purpose of this paper is to generate useful clinical knowledge that can be used to distinguish the outcomes of patients with delirium in long-term care facilities. For this purpose, we extracted the clinical classification knowledge associated with delirium using a local covering rule acquisition approach with the rough lower approximation region. The clinical applicability of the proposed method was verified using data collected from a prospective cohort study. From the results of this study, we found six useful clinical pieces of evidence that the duration of delirium could more than 12 days. Also, we confirmed eight factors such as BMI, Charlson Comorbidity Index, hospitalization path, nutrition deficiency, infection, sleep disturbance, bed scores, and diaper use are important in distinguishing the outcomes of delirium patients. The classification performance of the proposed method was verified by comparison with three benchmarking models, ANN, SVM with RBF kernel, and Random Forest, using a statistical five-fold cross-validation method. The proposed method showed an improved average performance of 0.6% and 2.7% in both accuracy and AUC criteria when compared with the SVM model with the highest classification performance of the three models respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.846-851
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• 2005

In this paper, we propose Genetic Algorithms(GAs)-based Optimized Polynomial Neural Networks(PNN). The proposed algorithm is based on Group Method of Data Handling(GMDH) method and its structure is similar to feedforward Neural Networks. But the structure of PNN is not fixed like in conventional neural networks and can be generated in a dynamic manner. As each node of PNN structure, we use several types of high-order polynomial such as linear, quadratic and modified quadratic, and it is connected as various kinds of multi-variable inputs. The conventional PNN depends on the experience of a designer that select the number of input variables, input variable and polynomial type. Therefore it is very difficult to organize optimized network. The proposed algorithm leads to identify and select the number of input variables, input variable and polynomial type by using Genetic Algorithms(GAs). The aggregate performance index with weighting factor is proposed as well. The study is illustrated with tile NOx omission process data of gas turbine power plant for application to nonlinear process. In the sequel the proposed model shows not only superb predictability but also high accuracy in comparison to the existing intelligent models.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2002.07a
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• pp.25-37
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• 2002

The most important industrial application of gamma radiation in characterizing green compacts is the determination of the density. Examples are given where this method is applied in manufacturing technical components in powder metallurgy. The requirements imposed by modern quality management systems and operation by the workforce in industrial production are described. The accuracy of measurement achieved with this method is demonstrated and a comparison is given with other test methods to measure the density. The advantages and limitations of gamma ray densitometry are outlined. The gamma ray densitometer measures the attenuation of gamma radiation penetrating the test parts (Fig. 1). As the capability of compacts to absorb this type of radiation depends on their density, the attenuation of gamma radiation can serve as a measure of the density. The volume of the part being tested is defined by the size of the aperture screeniing out the radiation. It is a channel with the cross section of the aperture whose length is the height of the test part. The intensity of the radiation identified by the detector is the quantity used to determine the material density. Gamma ray densitometry can equally be performed on green compacts as well as on sintered components. Neither special preparation of test parts nor skilled personnel is required to perform the measurement; neither liquids nor other harmful substances are involved. When parts are exhibiting local density variations, which is normally the case in powder compaction, sectional densities can be determined in different parts of the sample without cutting it into pieces. The test is non-destructive, i.e. the parts can still be used after the measurement and do not have to be scrapped. The measurement is controlled by a special PC based software. All results are available for further processing by in-house quality documentation and supervision of measurements. Tool setting for multi-level components can be much improved by using this test method. When a densitometer is installed on the press shop floor, it can be operated by the tool setter himself. Then he can return to the press and immediately implement the corrections. Transfer of sample parts to the lab for density testing can be eliminated and results for the correction of tool settings are more readily available. This helps to reduce the time required for tool setting and clearly improves the productivity of powder presses. The range of materials where this method can be successfully applied covers almost the entire periodic system of the elements. It reaches from the light elements such as graphite via light metals (AI, Mg, Li, Ti) and their alloys, ceramics ($AI_20_3$, SiC, Si_3N_4, $Zr0_2$, ...), magnetic materials (hard and soft ferrites, AlNiCo, Nd-Fe-B, ...), metals including iron and alloy steels, Cu, Ni and Co based alloys to refractory and heavy metals (W, Mo, ...) as well as hardmetals. The gamma radiation required for the measurement is generated by radioactive sources which are produced by nuclear technology. These nuclear materials are safely encapsulated in stainless steel capsules so that no radioactive material can escape from the protective shielding container. The gamma ray densitometer is subject to the strict regulations for the use of radioactive materials. The radiation shield is so effective that there is no elevation of the natural radiation level outside the instrument. Personal dosimetry by the operating personnel is not required. Even in case of malfunction, loss of power and incorrect operation, the escape of gamma radiation from the instrument is positively prevented.

• Journal of Biomedical Engineering Research
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• v.36 no.4
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• pp.115-122
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• 2015

A standard air flow generator system was developed to generate air flows of various levels simultaneously applied to two different air flow transducer modules. Axes of two identical standard syringes for spirometer calibration were connected with each other and driven by a servo-motor. Linear displacement transducer was also connected to the syringe axis to accurately acquire the volume change signal. The user can select either sinusoidal or square waveform of volume change and manually input any volume as well as maximal flow rate levels ranging 0~3 l and 0~15 l/s, respectively. Various volume and flow levels were input to operate the system, then the volume signal was acquired followed by numerical differentiation to obtain the air flow signal. The measured volumes and maximal air flow rates were compared with the user input data. The relative errors between the user-input and the measured stroke volumes were all within 0.5%, demonstrating very accurate driving of the system. In case of the maximal flow rate, relatively large error was observed when the syringe was driven very fast within a very short time duration. However, except for these few data, most measured flow rates revealed relative errors of approximately 2%. When the measure and user-input stroke volume and maximal flow rate data were analyzed by linear regression analysis, respectively, the correlation coefficients were satisfactorily higher than 0.99 (p < 0.0001). These results demonstrate that the servo-motor controls the syringes with enough accuracy to generate standard air flows. Therefore, the present system would be very much practical for calibration process as well as performance evaluation and comparison of two different air flow transducer modules.

• The Journal of Engineering Geology
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• v.20 no.1
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• pp.35-45
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• 2010

The soil tests have been performed on the specimens obtained from about 1,150 sites including landslides and non-landslides areas in natural terrains for last 10 years. Based on the results of those tests, the average soil properties are estimated and the simple equations for estimating permeability are proposed according to geologic conditions. The average permeability in Granite and Mudstone sites is higher than other sites and the content of silt and clay in Mudstone and Gneiss sites is higher than other sites. The correlation analysis and the regression analysis were performed to estimate the coefficient of permeability according to geological conditions. As the result of the correlation analysis, the coefficient of permeability is selected as a dependent variable, and the silt and clay contents, the water contents and the dry unit weights are selected as independent variables. As the result of the regression analysis, the silt and clay contents and the void ratio were involved commonly in the linear regression equations according to geological conditions. To verify the proposed the linear regression equations, the measured result of the coefficient of permeability at other sites was compared with the result predicted with the proposed equations. As the result of comparison, there were a little bit different between them for some data. However the difference was relatively small. Therefore, the linear regression equations for estimating the coefficient of permeability according to geological conditions may be applied to Korean soils. However, these equations should be verified and corrected continuously to improve the accuracy.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1077-1093
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• 2020

Sea Surface Temperature (SST) is an important environmental indicator that affects climate coupling systems around the world. In particular, coastal regions suffer from abnormal SST resulting in huge socio-economic damage. This study used Long Short Term Memory (LSTM) and Convolutional Long Short Term Memory (ConvLSTM) to predict SST up to 7 days in the south sea region in South Korea. The results showed that the ConvLSTM model outperformed the LSTM model, resulting in a root mean square error (RMSE) of 0.33℃ and a mean difference of -0.0098℃. Seasonal comparison also showed the superiority of ConvLSTM to LSTM for all seasons. However, in summer, the prediction accuracy for both models with all lead times dramatically decreased, resulting in RMSEs of 0.48℃ and 0.27℃ for LSTM and ConvLSTM, respectively. This study also examined the prediction of abnormally high SST based on three ocean heatwave categories (i.e., warning, caution, and attention) with the lead time from one to seven days for an ocean heatwave case in summer 2017. ConvLSTM was able to successfully predict ocean heatwave five days in advance.