• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.255-265
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• 2019

In the present study, the effect of opening patterns of a flow control valve on underwater discharge systems using a linear pump was investigated numerically. For that, a improved mathematical model was developed. The improvement is to separate a middle tank from a water cylinder because the cross-section area of the inlet of the middle tank is an important parameter. To validate the improved model, calculation results were compared with a previous study. The results showed that $2^{nd}$ order or more polynomial opening patterns had an advantage over ramp opening patterns. Higher an order of polynomial resulted in wider operating limits. An escape velocity and a maximum acceleration of underwater vehicle were affected by time derivative of the cross-section area of the flow control valve. Besides, as a velocity profile of the vehicle got closer to linearity, the escape velocity got faster and the maximum acceleration got smaller. And velocities of the vehicle and piston had similar variation trend.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• Communications of Mathematical Education
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• v.27 no.3
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• pp.205-220
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• 2013

In this research, in order to investigate the principles for the development of mathematics textbook for decision-making based on storytelling, we conceptualized the educational meaning of decision-making and specified the principles and the methods for the textbook based on decision-making. We illustrated the principles and the methods by the cases from the model textbook for the conditional probability that we have developed. We discussed the implication for the future development and implementation of mathematics textbook for decision-making based on storytelling.

• Membrane and Water Treatment
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• v.7 no.2
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• pp.127-141
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• 2016

Electrodialysis (ED) is known to be a useful membrane process for desalination, concentration, separation, and purification in many fields. In this process, it is desirable to work at high current density in order to achieve fast desalination with the lowest possible effective membrane area. In practice, however, operating currents are restricted by the occurrence of concentration polarization phenomena. Many studies showed the occurrence of a limiting current density (LCD). The limiting current density in the electrodialysis process is an important parameter which determines the electrical resistance and the current utilization. Therefore, its reliable determination is required for designing an efficient electrodialysis plant. The purpose of this study is the development of a predictive model of the limiting current density in an electrodialysis process using response surface methodology (RSM). A two-factor central composite design (CCD) of RSM was used to analyze the effect of operation conditions (the initial salt concentration (C) and the linear flow velocity of solution to be treated (u)) on the limiting current density and to establish a regression model. All experiments were carried out on synthetic brackish water solutions using a laboratory scale electrodialysis cell. The limiting current density for each experiment was determined using the Cowan-Brown method. A suitable regression model for predicting LCD within the ranges of variables used was developed based on experimental results. The proposed mathematical quadratic model was simple. Its quality was evaluated by regression analysis and by the Analysis Of Variance, popularly known as the ANOVA.

• Food Science and Biotechnology
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• v.16 no.2
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• pp.238-242
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• 2007

Square root models were developed for predicting the kinetics of growth of Listeria monocytogenes in sesame leaves as a function of temperature (4, 10, or $25^{\circ}C$). At these storage temperatures, the primary growth curves fit well ($R^2=0.898$ to 0.980) to a Gompertz equation to obtain lag time (LT) and specific growth rate (SGR). The square root models for natural logarithm transformations of the LT and SGR as a function of temperature were obtained by SAS's regression analysis. As storage temperature ($4-25^{\circ}C$) decreased, LT increased and SGR decreased, respectively. Square root models were identified as appropriate secondary models for LT and SGR on the basis of most statistical indices such as coefficient determination ($R^2=0.961$ for LT, 0.988 for SGR), mean square error (MSE=0.l97 for LT, 0.005 for SGR), and accuracy factor ($A_f=1.356$ for LT, 1.251 for SGR) although the model for LT was partially not appropriate as a secondary model due to the high value of bias factor ($B_f=1.572$). In general, our secondary model supported predictions of the effects of temperature on both LT and SGR for L. monocytogenes in sesame leaves.

• Journal of the Korean School Mathematics Society
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• v.10 no.4
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• pp.557-571
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• 2007

The purpose of this paper was to develope a model for enhancement of mathematics education using participatory mathematics. Traditionally, mathematics has been considered ready-made and students need to practice it without real applications of mathematics. The 6th grade students in the two classrooms participated in the 60 class hours and the researcher and observers investigated students' achievements and reactions. In this model, students actively apply mathematics to real-life problems and futhermore change our life, which is one of the unique elements. Thus, students can experience mathematical power while they do mathematics. Every student need to experience with this model several times in a semester so that he or she can be active a citizen to change society a better place.

• Journal of Sensor Science and Technology
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• v.5 no.6
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• pp.35-42
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• 1996

In this paper, a development of model based battery SOC indicator is described. The proposed method is independent upon initial SOC, is reliable on the sudden change of load, and could estimate the available driving distance. The mathematical model of battery which has relation of the current, voltage and SOC estimates the SOC by least square estimation to minimize the error between measured voltage and estimated voltage. For experiment, the charging and discharging system using computer was designed to acquire the current and voltage data for model. The feasibility in electric vehicle was confirmed by variable load testing using the developed SOC indicator by stand-alone type microcontroller.

• Journal of Distribution Science
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• v.14 no.2
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• pp.5-12
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• 2016

Purpose - The present study examines the interrelationship between various components constituting shopping experience in the context of the Indian shopping malls. Research design, data, and methodology - Extracting components of shopping experience from the literature review, the study used Interpretive Structural Modelling (ISM) to propose a conceptual model. The study adopted a mixed methods research involving theoretical constructs from past research, qualitative assessment of relationship between the constructs and imposing definite order and direction to qualitative relations based on mathematical computations. Results - Proposed model indicates that the five components of shopping experience (ambience, physical infrastructure, convenience, marketing focus and safety and security) do not converge directly into shopping experience. Rather, they operate following a hierarchy of influences in which marketing focus plays the role of the initiator. Conclusions - This model points at the order of preference of different components of shopping experience and can be a useful guide for retail industry, especially mall developers and supermarket/hypermarket, may use the findings in key decisions about development of physical infrastructure, which are based on marketing focus.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.3
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• pp.151-158
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• 2002

Automotive engineers involved in a new car project use various CAD systems that are chosen based on work requirements. For example, engineers in Hyundai Motors are using Pro/Designer and Alias fur the style design, but they use CATIA to design parts and assemblies, ANSYS for FEM analysis, and Pro/Engineer to design engines. Because they use different CAD systems, they have difficulties in collaborative design. Data, which contains errors, is transferred between CAD systems. It is difficult to find out such errors in a large CAD model. An evaluation method for CAD models has been developed in this study. This diagnosis tool analyses a STEP or an IGES file generated from a CAD system, and produces a quantitative error report. The tool has been tested with actual data sets. This paper proposes an algorithm that produces mathematical error values of entities of IGES models that have geometrical data, and entities of STEP models that have topological data, and inspects every part off model. To develop this system, we have used the OpenCASCADE kernel, which is an open source kernel developed by Matra Datavision of France.

• Journal of Construction Engineering and Project Management
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• v.4 no.3
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• pp.13-20
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• 2014

Estimating time and cost is an important mission in the early phase of a construction project, especially in feasibility study. It provides a foundation for making decision whether or not the project is performed on schedule and within budget. Thus, reliability of this estimate plays a key role in measuring the success of a project. This study was carried out to investigate the time-cost relationship in building construction projects in Bangladesh. The mathematical equation used in this study is based on Bromilow's equation. The research data were collected from sixty-three completed building projects through questionnaire survey. Type of clients, type of projects, and tender methods are the project characteristics considered in this study. The results of analysis indicated that the Bromilow's time-cost (BTC) models developed for each project characteristic are appropriate due to quite high coefficient of determination and relatively small mean percent errors. Among them, the forecasted model for time and cost according to tender methods is the best fit model. It is concluded that the BTC model could be applied in building construction project to predict its time and cost in Bangladesh. Four different regression models were also developed in this study. The results of BTC model between some selected countries were compared to gain the comprehensive view.