• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.10
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• pp.3-9
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• 2016

Current mobile communication systems utilize the multiple-input multiple-output (MIMO) transmission technique as an important means to enhance the bandwidth efficiency. Accurate beamforming via channel estimation contributes to the signal-to-interference-plus-noise ratio (SINR) increase and the system performance improvement when MIMO transmission techniques are employed. Therefore, determination of beamforming vectors as well as the design of appropriate codebooks defining these codevectors play an important role in system operation. In this paper, we statistically analyze the phase difference between the channels corresponding to adjacent antenna elements in order to design an efficient codebook for uniform circular arrays (UCAs). We introduce new parameters which compensate for the additional phase difference observed in its probability density functions (PDFs). The performance of the proposed codebook is tested using the spatial channel model (SCM) to demonstrate its gain over the standard codebooks adopted in the long term evolution (LTE) Releases 8 and 10.

• Smart Structures and Systems
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• v.21 no.5
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• pp.705-713
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• 2018

High-speed rail (HSR) has been in operation and development in many countries worldwide. The explosive growth of HSR has posed great challenges for operation safety and ride comfort. Among various technological demands on high-speed trains, vibration is an inevitable problem caused by rail/wheel imperfections, vehicle dynamics, and aerodynamic instability. Ride comfort is a key factor in evaluating the operational performance of high-speed trains. In this study, online monitoring data have been acquired from an in-service high-speed train for condition assessment. The measured dynamic response signals at the floor level of a train cabin are processed by the Sperling operator, in which the ride comfort index sequence is used to identify the train's operation condition. In addition, a novel technique that incorporates salient features of Bayesian inference and time series analysis is proposed for outlier detection and change detection. The Bayesian forecasting approach enables the prediction of conditional probabilities. By integrating the Bayesian forecasting approach with time series analysis, one-step forecasting probability density functions (PDFs) can be obtained before proceeding to the next observation. The change detection is conducted by comparing the current model and the alternative model (whose mean value is shifted by a prescribed offset) to determine which one can well fit the actual observation. When the comparison results indicate that the alternative model performs better, then a potential change is detected. If the current observation is a potential outlier or change, Bayes factor and cumulative Bayes factor are derived for further identification. A significant change, if identified, implies that there is a great alteration in the train operation performance due to defects. In this study, two illustrative cases are provided to demonstrate the performance of the proposed method for condition assessment of high-speed trains.

• 한국연소학회:학술대회논문집
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• 2003.12a
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• pp.103-108
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• 2003

The effect of equivalence ratio and fuel/air mixing quality on the phase-resolved gas temperatures at different phases of the oscillating pressure cycle was experimentally investigated. An atmospheric pressure, optically accessible and laboratory-scale dump combustor operating on methane with heat release rate of 1.59kW was used. Temperature measurements were made using coherent anti-Stokes Raman spectroscopy (CARS) at several spatial locations for typical unstable combustion conditions. Analysis was conducted using parameters such as phase-resolved averaged temperature, normalized standard deviation and temperature probability distribution functions (PDFs). Also the probability on the occurrence of high temperature (over 1900K) was investigated to get the information on the perturbation of equivalence ratio and NOx emission characteristics. It was shown that most of temperature histograms exhibit Gaussian profile which has short breadth of temperature fluctuation at equivalence ratio of 0.6, while beta profile was predominant for the cases of other equivalence ratios (${\Phi}$=0.55, 0.50). The characteristics on the occurrence of high temperature also displayed periodic wave form which is very similar to the pressure signal. And the amplitude of this profile goes larger as the fuel/air mixing quality become poorer. These also provided additional information on the perturbation of equivalence ratio at flame as well as NOx emission characteristics.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.6
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• pp.75-82
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• 2005

The robust feature vector selection method for the multiple frontal face detection is proposed in this paper. The proposed feature vector for the training and classification are integrated by means, amplitude projections, and its 1D Harr wavelet of the input image. And the statistical modeling is performed both for face and nonface classes. Finally, the estimated probability density functions (PDFs) are applied for the detection of multiple frontal faces in the still image. The proposed method can handle multiple faces, partially occluded faces, and slightly posed-angle faces. And also the proposed method is very effective for low quality face images. Experimental results show that detection rate of the propose method is $98.3\%$ with three false detections on the testing data, SET3 which have 227 faces in 80 images.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.225-233
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• 2022

Due to the large number of documents that are important to people and many of their requests from time to time to perform an essential official procedure, this requires a practical arrangement and organization for them. When necessary, many people struggle with effectively arranging official documents that enable display, which takes a lot of time and effort. Also, no mobile apps specialize in professionally preserving essential electronic records and displaying them when needed. Dataset consisting of 10,841 rows and 13 columns was analyzed using Anaconda, Python, and Mito Data Science new tool obtained from Google Play. The research was conducted using the quantitative descriptive approach. The presented solution is a model specialized in saving essential documents, categorizing according to the user's desire, and displaying them when needed. It is possible to send in an image or a pdf file. Aside from identifying file kinds like PDFs and pictures, the model also looks for and verifies specific file extensions. The file extension and its properties are checked before sharing or saving it by applying the similarity algorithm (Levenshtein). Our method effectively and efficiently facilitated the search process, saving the user time and effort. In conclusion, such an application is not available, which facilitates the process of classifying documents effectively and displaying them quickly and easily for people for printing or sending to some official procedures, and it is considered one of the applications that greatly help in preserving time, effort, and money for people.

• Korean Journal of Heritage: History & Science
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• v.53 no.3
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• pp.4-23
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• 2020

In this article, the excavation and research of cultural assets were set as a process and the improvement measures were considered. To this end, we examined the process of excavating cultural assets to diagnose problems, suggested changes in the format of reports and the establishment of a database, and drew up improvement models for Silla's ancient tombs and research. The problems of the current process of excavating cultural assets are as follows. First, investigation and research fail to integrate and merely comprise 'examination as an administrative procedure' or 'investigation for the sake of investigation', which ultimately hamper research and achievement. Second, there are differences in the composition or description of the report by surveyors or excavation agencies, which make it difficult to integrate data at a higher level. Third, the current form of reporting remains in analog format such as books and PDFs, which not only reduces continuity and efficiency to the research phase, but also lags behind the rapidly changing times. We believe that the improvement of these problems should be achieved by computerizing reports, converting them into digital formats, and establishing them in a database. First, regarding the transition to report format, it was pointed out that the form of excavation data, the final stage of the excavation process, remains analog and the improvement model was presented from the perspective of linking it to excavation and research, and the justification was emphasized through comparison with other cases. Second, the database reviewed the build model for Silla tombs. To this end, the purpose and expected effects, targets, progress, attributes, categories, and interfaces were examined.

• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.380-386
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• 2002

Conventional post-processing as like confidence measure (CM) proposed by Rahim calculates phones' CM using the likelihood between phoneme model and anti-model, and then word's CM is obtained by averaging phone-level CMs[1]. In conventional method, CMs of some specific keywords are tory low and they are usually rejected. The reason is that statistics of phone-level CMs are not consistent. In other words, phone-level CMs have different probability density functions (pdf) for each phone, especially sri-phone. To overcome this problem, in this paper, we propose normalized confidence measure. Our approach is to transform CM pdf of each tri-phone to the same pdf under the assumption that CM pdfs are Gaussian. For evaluating our method we use common keyword spotting system. In that system context-dependent HMM models are used for modeling keyword utterance and contort-independent HMM models are applied to non-keyword utterance. The experiment results show that the proposed NCM reduced FAR (false alarm rate) from 0.44 to 0.33 FA/KW/HR (false alarm/keyword/hour) when MDR is about 8%. It achieves 25% improvement of FAR.

• Economic and Environmental Geology
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• v.29 no.6
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• pp.713-724
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• 1996

The time-series resident solute concentrations, monitored at two field plots using the automated 144-channel TDR system by Kim (this issue), are used to investigate the dominant transport mechanism at field scale. Two models, based on contradictory assumptions for describing the solute transport in the vadose zone, are fitted to the measured mean breakthrough curves (BTCs): the deterministic one-dimensional convection-dispersion model (CDE) and the stochastic-convective lognormal transfer function model (CLT). In addition, moment analysis has been performed using the probability density functions (pdfs) of the travel time of resident concentration. Results of moment analysis have shown that the first and second time moments of resident pdf are larger than those of flux pdf. Based on the time moments, expressed in function of model parameters, variance and dispersion of resident solute travel times are derived. The relationship between variance or dispersion of solute travel time and depth has been found to be identical for both the time-series flux and resident concentrations. Based on these relationships, the two models have been tested. However, due to the significant variations of transport properties across depth, the test has led to unreliable results. Consequently, the model performance has been evaluated based on predictability of the time-series resident BTCs at other depths after calibration at the first depth. The evaluation of model predictability has resulted in a clear conclusion that for both experimental sites the CLT model gives more accurate prediction than the CDE model. This suggests that solute transport at natural field soils is more likely governed by a stream tube model concept with correlated flow than a complete mixing model. Poor prediction of CDE model is attributed to the underestimation of solute spreading and thus resulting in an overprediction of peak concentration.

• Journal of Nutrition and Health
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• v.46 no.1
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• pp.50-60
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• 2013

The aims of this study were to estimate daily intake of macrominerals from beverages, liquid teas, and liquid coffees and to evaluate their potential health risks for Korean children and adolescents (1-to 19 years old). Assessment of dietary intake was conducted using the actual level of sodium, calcium, phosphorus, potassium, and magnesium in non-alcoholic beverages and (207 beverages, 19 liquid teas, and 24 liquid coffees) the food consumption amount drawn from "The Fourth Korea National Health and Nutrition Examination Survey (2007-2009)". To estimate the dietary intake of non-alcoholic beverages, 6,082 children and adolescents (Scenario I) were compared with 1,704 non-alcoholic beverage consumption subjects among them (Scenario II). Calculation of the estimated daily intake of macrominerals was based on point estimates and probabilistic estimates. The values of probabilistic macromineral intake, which is a Monte-Carlo approach considering probabilistic density functions of variables, were presented using the probabilistic model. The level of safety for macrominerals was evaluated by comparison with population nutrient intake goal (Goal, 2.0 g/day) for sodium, tolerable upper intake level (UL) for calcium (2,500 mg/day) and phosphorus (3,000-3,500 mg/day) set by the Korean Nutrition Society (Dietary Reference Intakes for Koreans, KDRI). For total children and adolescents (Scenario I), mean daily intake of sodium, calcium, phosphorus, potassium, and magnesium estimated by probabilistic estimates using Monte Carlo simulation was, respectively, 7.93, 10.92, 6.73, 23.41, and 1.11, and 95th percentile daily intake of those was, respectively, 28.02, 44.86, 27.43, 98.14, and 3.87 mg/day. For consumers-only (Scenario II), mean daily intake of sodium, calcium, phosphorus, potassium, and magnesium estimated by probabilistic estimates using Monte Carlo simulation was, respectively, 19.10, 25.77, 15.83, 56.56, and 2.86 mg/day, and 95th percentile daily intake of those was, respectively, 62.67, 101.95, 62.09, 227.92, and 8.67 mg/day. For Scenarios I II, sodium, calcium, and phosphorus did not have a mean an 95th percentile intake that met or exceeded the 5% of Goal and UL.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.8
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• pp.1304-1316
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• 2014

The aims of this study were to estimate daily intakes of artificial sweeteners from beverages and liquid teas as well as evaluate their potential health risks in Korean children and adolescents (1 to 19 years old). Dietary intake assessment was conducted using actual levels of aspartame, acesulfame-K, and sucralose in non-alcoholic beverages (651 beverages and 87 liquid teas), and food consumption amounts were drawn from "The Fourth Korea National Health and Nutrition Examination Survey (2007~2009)". To estimate dietary intake of non-alcoholic beverages, a total of 6,082 children and adolescents (Scenario I) were compared to 1,704 non-alcoholic beverage consumption subjects (Scenario II). The estimated daily intake of artificial sweeteners was calculated based on point estimates and probabilistic estimates. The values of probabilistic artificial sweeteners intakes were presented by a Monte Carlo approach considering probabilistic density functions of variables. The level of safety for artificial sweeteners was evaluated by comparisons with acceptable daily intakes (ADI) of aspartame (0~40 mg/kg bw/day), acesulfame-K (0~15 mg/kg bw/day), and sucralose (0~15 mg/kg bw/day) set by the World Health Organization. For total children and adolescents (Scenario I), mean daily intakes of aspartame, acesulfame-K, and sucralose estimated by probabilistic estimates using Monte Carlo simulation were 0.09, 0.01, and 0.04 mg/kg bw/day, respectively, and 95th percentile daily intakes were 0.30, 0.02, and 0.13 mg/kg bw/day, respectively. For consumers-only (Scenario II), mean daily intakes of aspartame, acesulfame-K, and sucralose estimated by probabilistic estimates using Monte Carlo simulation were 0.52, 0.03, and 0.22 mg/kg bw/day, respectively, and 95th percentile daily intakes were 1.80, 0.12, and 0.75 mg/kg bw/day, respectively. For scenarios I and II, neither aspartame, acesulfame-K, nor sucralose had a mean and 95th percentile intake that exceeded 5.06% of ADI.