• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.5
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• pp.407-417
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• 2019

When a free-falling ballistic missile intercepts a maritime target in a sea clutter environment at high grazing angle, detection performance of the ballistic missile's seeker can be rapidly degraded by the effect of sea clutter. To solve this problem, it is necessary to verify the performance of maritime target detection via simulations based on various scenarios. We accomplish this by applying a two-dimensional cell -averaging constant false alarm rate detector to a two-dimensional radar image, which is generated by merging a sea clutter signal at high grazing angle with a maritime target signal corresponding to the signal-to-clutter ratio. Simulation results using a computer-aided design model and commercial numerical electromagnetic solver in various scenarios show that the performance of maritime target detection significantly depends on the grazing and azimuth angles.

• Journal of Korean Academy of Nursing
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• v.53 no.3
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• pp.324-339
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• 2023

Purpose: This study aimed to valuate the reliability and validity of the Korean version of the 5C Psychological Antecedents of Vaccination (K-5C) scale. Methods: The English version of the 5C scale was translated into Korean, following the World Health Organization guidelines. Data were collected from 316 community-dwelling adults. Content validity was evaluated using the content validity index, while construct validity was evaluated through confirmatory factor analysis. Convergent validity was examined by assessing the correlation with vaccination attitude, and concurrent validity was evaluated by examining the association with coronavirus disease 2019 (COVID-19) vaccination status. Internal consistency and test-retest reliability were also evaluated. Results: Content validity results indicated an item-level content validity index ranging from .83 to 1, and scale-level content validity index, averaging method was .95. Confirmatory factor analysis supported the fit of the measurement model, comprising a five-factor structure with a 15-item questionnaire (RMSEA = .05, SRMR = .05, CFI = .97, TLI = .96). Convergent validity was acceptable with a significant correlation between each sub-scale of the 5C scale and vaccination attitude. In concurrent validity evaluation, confidence, constraints, and collective responsibility of the 5C scale were significant independent predictors of the current COVID-19 vaccination status. Cronbach's alpha for each subscale ranged from .78 to .88, and the intraclass correlation coefficient for each subscale ranged from .67 to .89. Conclusion: The Korean version of the 5C scale is a valid and reliable tool to assess the psychological antecedents of vaccination among Korean adults.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3242-3265
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• 2023

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

• Journal of the Korea Concrete Institute
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• v.19 no.5
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• pp.585-593
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• 2007

This paper presents a model for durability evaluation of concrete structures exposed to marine environment, considering mainly a build-up of surface chloride $(C_s)$ as well as diffusion coefficient (D) and chloride threshold level $(C_{lim})$. In this study, time dependency of $C_s$ and D were extensively studied for more accurate evaluation of service life of concrete structures. An analytical solution to the Fick's second law was presented for prediction of chloride ingress for time varying $C_s$. For the time varying $C_s$, a refined model using a logarithm function for time dependent $C_s$ was proposed by the regression analysis, and averaging integrated values of the D with time over exposed duration were calculated and then used for prediction of the chloride ingress to consider time dependency of D. Durability design was also carried out for railway concrete structures exposed to marine environment to ensure 100 years of service life by using the proposed models along with the standard specification on durability in Korea. The proposed model was verified by the so-called performance-based durability design, which is widely used in Europe. Results show that the standard specification underestimates durability performances of concrete structures exposed to marine environment, so the cover depth design using current durability evaluation in the standard specifications is very much conservative. Therefore, it is found that utilizing proposed models considering time dependent characteristics of $C_s$ and D can evaluate service lift of concrete structures in marine environment more accurately.

• Journal of Wetlands Research
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• v.13 no.1
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• pp.79-94
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• 2011

As demonstrated in study for non-submerged groynes, the flow field is predominantly two-dimensional, with mainly horizontal eddies. The eddies shed form the tips of the groynes and migrate in the flow direction. These eddies have horizontal dimensions in the order of tens of meters and time-scales in the order of minutes. In the standard flow simulations, these motions are usually not resolved, due to a too coarse grid, too large time steps and, more importantly, the use of inadequate turbulence modelling. using for example a k-${\varepsilon}$ model, it is necessary to introduce substantial modifications. Therefore simulation resolved in this study, were carried out using the DELFT-3D-MOR programme, which is part of the DELFT3D software package of WL/Delft Hydraulics and In this study, apply a two-dimensional depth-averaged model, taking an horizontal large eddy simulation(HLES). The bed morphology computed when using HLES, as well as the associated time-scale, is similar to what has been obseved in a field case. When using a mean-flow model with-out HELS, the bed morphology is less realistic and the morphological time-scale is much larger. This slow development is the result of neglecting(or averaging). the strong velocity fluctuations associated with the time-varying eddy formation.

• Journal of Korea Water Resources Association
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• v.47 no.5
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• pp.435-446
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• 2014

Various drought indices developed from previous studies can not consider the inherent uncertainty of drought because they assess droughts using a pre-defined threshold. In this study, to consider inherent uncertainty embedded in monthly streamflow data, Hidden Markov Model (HMM) based drought index (HMDI) was proposed and then probabilistic assessment of hydrologic drought was performed using HMDI instead of using pre-defined threshold. Using monthly streamflow data (1966~2009) of Pyeongchang river and Upper Namhan river provided by Water Management Information System (WAMIS), applying the HMM after moving-averaging the data with 3, 6, 12 month windows, this study calculated the posterior probability of hidden state that becomes the HMDI. For verifying the method, this study compared the HMDI and Standardized Streamflow Index (SSI) which is one of drought indices using a pre-defined threshold. When using the SSI, only one value can be used as a criterion to determine the drought severity. However, the HMDI can classify the drought condition considering inherent uncertainty in observations and show the probability of each drought condition at a particular point in time. In addition, the comparison results based on actual drought events occurred near the basin indicated that the HMDI outperformed the SSI to represent the drought events.

• 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.

• Journal of the Korean GEO-environmental Society
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• v.19 no.11
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• pp.31-37
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• 2018

Most of tall building systems are composed of above-ground structure and underground structure used for parking and stores. The underground structure may have a pronounced influence on tall building response, but its influence is still not well understood. In a widely referred report on seismic design of tall buildings, it is recommended to model the underground structure ignoring the surrounding ground and to impose input ground motion calculated considering the underground structure-soil kinematic interaction between at its base. In this study, dynamic analyses are performed on 1B and 5B basements. The motions at the base are calculated to free field responses. The motions are further compared to two procedures outlined in the report to account for the kinematic interaction. It is shown that one of the procedure fits well for the 1B model, whereas both procedures provide poor fit with 5B model analysis result.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.699-708
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• 2017

The present study was to develop an approach for predicting soybean yield using a crop growth simulation model at the regional level where the detailed and site-specific information on cultivation management practices is not easily accessible for model input. CROPGRO-Soybean model included in Decision Support System for Agrotechnology Transfer (DSSAT) was employed for this study, and Illinois which is a major soybean production region of USA was selected as a study region. As a first step to predict soybean yield of Illinois using CROPGRO-Soybean model, genetic coefficients representative for each soybean maturity group (MG I~VI) were estimated through sowing date experiments using domestic and foreign cultivars with diverse maturity in Seoul National University Farm ($37.27^{\circ}N$, $126.99^{\circ}E$) for two years. The model using the representative genetic coefficients simulated the developmental stages of cultivars within each maturity group fairly well. Soybean yields for the grids of $10km{\times}10km$ in Illinois state were simulated from 2,000 to 2,011 with weather data under 18 simulation conditions including the combinations of three maturity groups, three seeding dates and two irrigation regimes. Planting dates and maturity groups were assigned differently to the three sub-regions divided longitudinally. The yearly state yields that were estimated by averaging all the grid yields simulated under non-irrigated and fully-Irrigated conditions showed a big difference from the statistical yields and did not explain the annual trend of yield increase due to the improved cultivation technologies. Using the grain yield data of 9 agricultural districts in Illinois observed and estimated from the simulated grid yield under 18 simulation conditions, a multiple regression model was constructed to estimate soybean yield at agricultural district level. In this model a year variable was also added to reflect the yearly yield trend. This model explained the yearly and district yield variation fairly well with a determination coefficients of $R^2=0.61$ (n = 108). Yearly state yields which were calculated by weighting the model-estimated yearly average agricultural district yield by the cultivation area of each agricultural district showed very close correspondence ($R^2=0.80$) to the yearly statistical state yields. Furthermore, the model predicted state yield fairly well in 2012 in which data were not used for the model construction and severe yield reduction was recorded due to drought.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.