• Korean Journal of Food Science and Technology
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• v.53 no.6
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• pp.739-748
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• 2021

This study focuses on the use of rice in the production of gluten-free rice udon (GFU) through an optimized mixing ratio, using the Box-Behnken response surface methodology (RSM). Different additional levels of rice flour (A, 40-60 g), acetylated distarch adipate (B, 10-20 g), and trehalose (C, 0-3 g) were used as variables, while water absorption level, volume, cooking loss, solid yield, lightness, texture properties, proximate compositions of GFU and turbidity of cooking water were set as responses in the RSM design model. The optimum mixing ratio for the preparation of gluten-free rice udon was obtained for 60.00 g of rice flour, 18.81 g of acetylated distarch adipate without the addition of trehalose. The response values of the optimized samples were water absorption (60.94%), volume (34.94%), turbidity of the cooking water (0.37), cooking loss (4.77%), solid yield (1.55 g), lightness value (70.04), hardness (2.53 N), springiness (0.18), gumminess (10.45 N), chewiness (1.83 N), and cohesiveness (2.89). This study has shown that rice flour can replace wheat flour to manufacture udon at an optimized mixing ratio successfully derived by statistical estimation method.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.607-618
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• 2019

A new and interesting task in statistics is to effectively analyze functional data that frequently comes from advances in modern science and technology in areas such as meteorology and biomedical sciences. Functional linear regression with scalar response is a popular functional data analysis technique and it is often a common problem to determine a functional association if a functional predictor variable affects the scalar response in the models. Recently, Kong et al. (Journal of Nonparametric Statistics, 28, 813-838, 2016) established classical testing methods for this based on functional principal component analysis (of the functional predictor), that is, the resulting eigenfunctions (as a basis). However, the eigenbasis functions are not generally suitable for regression purpose because they are only concerned with the variability of the functional predictor, not the functional association of interest in testing problems. Additionally, eigenfunctions are to be estimated from data so that estimation errors might be involved in the performance of testing procedures. To circumvent these issues, we propose a testing method based on fixed basis such as B-splines and show that it works well via simulations. It is also illustrated via simulated and real data examples that the proposed testing method provides more effective and intuitive results due to the localization properties of B-splines.

• Journal of Korea Water Resources Association
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• v.52 no.8
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• pp.555-563
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• 2019

Reduced thickness of the water pipes due to corrosion makes it difficult to perform the original functions since corrosion in metallic water pipes can occur over time. In this study, reliability model that can estimate the probability of pipe breakage is developed regarding corrosion depth increment according to service year. Probability of pipe breakage was calculated by FORM(First Order Reliability Method) and unsteady analysis was performed to analyze the statistical properties of water pressure. And KCIP(Korea Cast Iron Pipe) equation was adopted for the reliability function. Furthermore, change of pipe thickness was estimated by Nahal and Khelif equation and Romanoff equation. Therefore, pipe thickness was calculated due to change of corrosion depth and probability of pipe breakage was calculated and compared with 10, 20, 30 service years. From the results, probability of pipe breakage for network A is gradually increased from 6.8% to 8.6% according to service year of 10, 20, 30 when Nahal and Khelif equation is applied. And probability of pipe breakage for network A is also gradually increased from 6.4% to 8.9% according to service year of 10, 20, 30 when Romanoff equation is applied.

• Journal of the Korean Recycled Construction Resources Institute
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• v.7 no.1
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• pp.37-43
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• 2019

This research studied production of lightweight filling production for sink hole restoration utilizing various industrial by-products(2kinds of fly ash, petro-cokes CFBC ash, blast furnace slag fine particle). For this purpose, the mixed raw material properties(compressive strength) behaviors according to the blending ratio of industrial by-products were examined by applying the experimental design method and statistical analysis was performed using the commercial program MINITAB. Compressive strengths of industrial by-products were strongly dependent on blast furnace slag powder. Compressive strength(3days aging) was 3~11MPa depending on the amount of blast furnace slag powder used. The use of CFBC fly ash was evaluated to have the least effect on compressive strength. In addition, the compressive strength and the coefficient of permeability were measured by preparing foamed concrete for the experimental batch 1 condition in the mixture experimental design. In this case, the bulk density is 0.9 to 1.0, the apparent porosity is 30 to 50%, the compressive strength(3days old) is 1 to 2MPa, and the permeability coefficient is $10^{-2}$ to $10^{-3}cm/sec$.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.29-40
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• 2019

Categorical data with non-responses are frequently observed in election poll surveys, and can be represented by incomplete contingency tables. To estimate supporting rates of candidates, the identification of the missing mechanism should be pre-determined because the estimates of non-responses can be changed depending on the assumed missing mechanism. However, it has been shown that it is not possible to identify the missing mechanism when using observed data. To overcome this problem, sensitivity analysis has been suggested. The previously proposed sensitivity analysis can be applicable only to two-way incomplete contingency tables with binary variables. The previous sensitivity analysis is inappropriate to use since more than two of the factors such as region, gender, and age are usually considered in election poll surveys. In this paper, sensitivity analysis suitable to an multi-dimensional incomplete contingency table is devised, and also applied to the 19th Korean presidential election poll survey data. As a result, the intervals of estimates from the sensitivity analysis include actual results as well as estimates from various missing mechanisms. In addition, the properties of the missing mechanism that produce estimates nearest to actual election results are investigated.

• The Journal of the Korea Contents Association
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• v.22 no.6
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• pp.666-679
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• 2022

With the development of information and communication technology, borders are removed and consumer needs are diversified, so a new consumption form called overseas direct purchase has emerged and has been growing over the past few years. In this study, for the most common purchasing agency service among various types of overseas direct purchase, an empirical study was conducted on the effects of the content and logistics attributes of the overseas direct purchase platform on the perceived value and purchase intention of consumers. Data were collected from 273 domestic male and female adult consumers who had experience in information search related to overseas direct purchase, and the results of statistical analysis were summarized as follows. First, among the attributes of overseas direct purchase content and logistics attributes, only the attractiveness of content had a significant effect on the perceived value of consumers. Second, all perceived values of consumers were linked to purchase intentions, but among them, sensory values had a greater influence. Through these research results, it is suggested to increase the attractiveness of the web/app contents of overseas direct purchase service agents, and to improve the quality of services that can arouse sensory consumption experiences to meet the changing needs of consumers.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.259-270
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• 2021

Recently, interests in the use of Sharing Bike is increasing in consideration of eco-friendly transportation and safety from viruses. As the technology for collecting and storing data is improved with the development of ICTs, research on mobility using the Sharing Bike Data is also actively progressing. Therefore, this paper analyzes the properties of Sharing Bike Data and cases of researches on it through literature review, and based on the results of the review, data of Eoulling, the Sharing Bike System of Sejong City is analyzed as a way to utilize Sharing Bike Data. Most of the selected literature used structured data, and analyzed it through statistical methods or data mining. Through data analysis, it identified the current status, found out problems of the Sharing Bike System, proposed a solution to solve them, developed plans to activate the use of Sharing Bike. This provides basic data for efficient management and operation plans for Sharing Bike System. Ultimately, it will be possible to explore ways to improve mobility in urban spaces by utilizing Sharing Bike Data.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.389-398
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• 2022

Many studies have been conducted to accurately predict the correlations between As and heavy metals content in contaminated soil and cultivated crops; however, due to the low correlation between the two, few clear results were obtained to date. This study aimed to create statistical models that predict the As content transferred from soil to polished rice, considering the physicochemical properties of the soil, as well as the total content and the single-extracted content of As in the soil. Predictive models were derived through regression analysis while sequentially classifying soil samples according to pH, soluble As content by single extraction, and organic matter content of the soil. The correlation coefficients between the As content in 80 polished rice and total As content and Mehlich soluble As content in the soil were low, 0.533 and 0.493, respectively. However, the models derived after sequential classification of the soil by pH, a ratio of total As content to Mehlich soluble As content, and organic matter content greatly increased the predictive power; ① 0.963 for 13 soils with a pH higher than 6.5, ② 0.849 for 15 soils with pH lower than 6.5 and a high ratio of As_Tot/As_Mehlich, ③ 0.935 for 30 soils with pH lower than 6.5, a high ratio of As_Tot/As_Mehlich, and organic matter content lower than 8.5%. The suggested prediction model of As transfer from soil to polished rice derived by soil classification may serve as a statistically significant methodology in establishing a rice cultivation standard for arsenic-contaminated soil.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.163-171
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• 2009

Classical linear models have been generally used to analyze and forecast hydrologic time series. However, there is growing evidence of nonlinear structure in natural phenomena and hydrologic time series associated with their patterns and fluctuations. Therefore, the classical linear techniques for time series analysis and forecasting may not be appropriate for nonlinear processes. In recent, the BDS (Brock-Dechert-Scheinkman) statistic instead of conventional techniques has been used for detecting nonlinearity of time series. The BDS statistic was derived from the statistical properties of the correlation integral which is used to analyze chaotic system and has been effectively used for distinguishing nonlinear structure in dynamic system from random structures. DVS (Deterministic Versus Stochastic) algorithm has been used for detecting chaos and stochastic systems and for forecasting of chaotic system. This study showed the DVS algorithm can be also used for detecting nonlinearity of the time series. In this study, the stochastic and hydrologic time series are analyzed to detect their nonlinearity. The linear and nonlinear stochastic time series generated from ARMA and TAR (Threshold Auto Regressive) models, a daily streamflow at St. Johns river near Cocoa, Florida, USA and Great Salt Lake Volume (GSL) data, Utah, USA are analyzed, daily inflow series of Soyang dam and the results are compared. The results showed the BDS statistic is a powerful tool for distinguishing between linearity and nonlinearity of the time series and DVS plot can be also effectively used for distinguishing the nonlinearity of the time series.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.