• Proceedings of the Korean System Dynamics Society
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• 1999.08a
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• pp.105-132
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• 1999

If a system such as a port has a large boundary and complexity, and the system's substance is considered as a black box, forecast accuracy will be very low. Furthermore various components in a port exert significant influence on each other. To copy with these problem the form of structure models were introduced by using SD method. The Competitive Ports Model had several sub-systems consisting of each Unit Port models, and each Unit Port model was made by quantitative, qualitative factors and their feedback loops. The fact that all components of one port have influence on the components of the other ports should be taken into account to construct Competitive Port Models. However, with the current approach that is impossible, and in this paper, therefore, models were simplified by HFP adapted to integrate level variables of unit port models. Although many studies on modelling of port competitive situation have been conducted, both theoretical frame and methodology are still very weak. In this study, a new algorithm called ESD(Extensional System Dynamics) for the evaluation of port competition was presented, and applied to simulate port systems in northeast Asia.

• Structural Engineering and Mechanics
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• v.31 no.1
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• pp.57-74
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• 2009

Power signals resulting from spindle and feed motor, present a rich content of physical information, the appropriate analysis of which can lead to the clear identification of the nature of the tool wear. The partial least-squares regression (PLSR) method has been established as the tool wear analysis method for this purpose. Firstly, the results of the application of widely used techniques are given and their limitations of prior methods are delineated. Secondly, the application of PLSR is proposed. The singular value theory is used to noise reduction. According to grey relational degree analysis, sample variable is filtered as part sample variable and all sample variables as independent variables for modelling, and the tool wear is taken as dependent variable, thus PLSR model is built up through adapting to several experimental data of tool wear in different milling process. Finally, the prediction value of tool wear is compare with actual value, in order to test whether the model of the tool wear can adopt to new measuring data on the independent variable. In the new different cutting process, milling tool wear was predicted by the methods of PLSR and MLR (Multivariate Linear Regression) as well as BPNN (BP Neural Network) at the same time. Experimental results show that the methods can meet the needs of the engineering and PLSR is more suitable for monitoring tool wear.

• Journal of the Society of Naval Architects of Korea
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• v.53 no.1
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• pp.29-36
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• 2016

In shipbuilding and offshore plant construction, pipe-stools of various types are installed. Moreover, these are many quantities but they must be installed in a successive manner. Due to these characteristics the pipe-stool installation processes easily tends to cause the schedule delays in the overall production processes. In order to reduce delay, the goal of this study is to predicts production’s lead time before manufacturing. Through this predictions it’s expected to reduce total production’s lead time by improving it's process. First of all, we made MLR(Multiple Linear Regression) and PLSR(Partial Least Square Regression) model to predict pipe-spool's lead time and then compared predictability of MLR and PLSR model. If a explanatory variable is added, it will be possible to predict results precisely.

• Wind and Structures
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• v.34 no.4
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• pp.321-334
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• 2022

Irregularity in plan shape is very common for any type of building as it enhances better air ventilation for the inhabitants. Systematic opening at the middle of the facades makes the appearance of the building plan as a butterfly one. The primary focus of this study is to forecast the force, moment and torsional coefficient of a butterfly plan shaped tall building. Initially, Computational Fluid Dynamics (CFD) study is done on the building model based on Reynolds averaged Navier Stokes (RANS) k-epsilon turbulence model. Fifty random cases of irregularity and angle of attack (AOA) are selected, and the results from these cases are utilised for developing the surrogate models. Parametric equations are predicted for all these aerodynamic coefficients, and the training of these outcomes are also done for developing Artificial Neural Networks (ANN). After achieving the target acceptance criteria, the observed results are compared with the primary CFD data. Both parametric equations and ANN matched very well with the obtained data. The results are further utilised for discussing the effects of irregularity on the most critical wind condition.

• Computers and Concrete
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• v.29 no.6
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• pp.361-374
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• 2022

On the premise of ensuring that the automatic and quantitative discharging function of concrete conveyors is met, the accuracy of the weight forecast by the mathematical model of the screw conveying volume is improved, and the error of the weight of the concrete parts and the accumulation thickness is reduced. In this paper, the discrete element method (DEM) is used to simulate the macroscopic flow of concrete. Using the concrete discrete element model, the size of the screw conveyor is set, and establish the response model between the influencing factors (process and structure) and the concrete mass flow rate according to the design points of the screw discharging experiment. The nonlinear data fitting method is used to obtain the volumetric efficiency function under the influence of process and structural factors, and the traditional screw conveying volume model is improved. The mass flow rate of concrete predicted by the improved mathematical model of screw conveying volume is consistent with the test results. The model can accurately describe the conveying process of concrete and achieve the purpose of improving the accuracy of forecasting the weight of discharged concrete.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.37-46
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• 2023

The main aim of the article is to solve the problem of automating price monitoring using marketing forecasting methods and Excel functionality under martial law. The study used the method of algorithms, trend analysis, correlation and regression analysis, ANOVA, extrapolation, index method, etc. The importance of monitoring consumer price developments in market pricing at the macro and micro levels is proved. The introduction of a Dummy variable to account for the influence of martial law in market pricing is proposed, both in linear multiple regression modelling and in forecasting the components of the Consumer Price Index. Experimentally, the high reliability of forecasting based on a five-factor linear regression model with a Dummy variable was proved in comparison with a linear trend equation and a four-factor linear regression model. Pessimistic, realistic and optimistic scenarios were developed for forecasting the Consumer Price Index for the situation of the end of the Russian-Ukrainian war until the end of 2023 and separately until the end of 2024.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.147-154
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• 2024

The continuous increase in urban population due to migration of mases from rural areas to big cities has set urban water supply under serious stress. Urban water resources face scarcity of available water quantity, which ultimately effects the water supply. It is high time to address this challenging problem by taking appropriate measures for the improvement of water utility services linked with better understanding of demand side management (DSM), which leads to an effective state of water supply governance. We propose a dynamic framework for preventive DSM that results in optimization of water resource management. This paper uses Agent Based Modeling (ABM) with Digital Twin (DT) to model water consumption behavior of a population and consequently forecast water demand. DT creates a digital clone of the system using physical model, sensors, and data analytics to integrate multi-physical quantities. By doing so, the proposed model replicates the physical settings to perform the remote monitoring and controlling jobs on the digital format, whilst offering support in decision making to the relevant authorities.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.163-172
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• 2012

Modern society, various great strength crimes are producing. After all crimes happen, it is most important that prevent crime beforehand than that cope. So, many research studied to prevent various crime. However, existing method of studies are to analyze and prevent by society and psychological factors. Therefore we wishes to achieve research to forecast crime by time using Markov chain method. We embody modelling for crime occurrence estimate by crime type time using crime occurrence number of item data that is collected about 5 great strength offender strength, murder, rape, moderation, violence. And examined propriety of crime occurrence estimate modelling by time that propose in treatise that compare crime occurrence type crime occurrence estimate price and actuality occurrence value. Our proposed crime occurrence estimate techniques studied to apply maximum value by critcal value about great strength crime such as strength, murder, rape etc. actually, and heighten crime occurrence estimate probability by using way to apply mean value about remainder crime in this paper. So, we wish to more study about wide crime case and as the crime occurrence estimate rate and actuality value by time are different in crime type hereafter applied examples investigating.

• KIEAE Journal
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• v.11 no.3
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• pp.63-68
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• 2011

Building deterioration would be proceeded by various causes such as physical, social, economic degradation. The deterioration would be inevitably prevented or delayed to get the decent function and performance in various building part and components. The maintenance and management are continued to provide the decent living condition for the household. The maintenance means mainly a repair, including the on-time and longterm plan. The longterm repair would be conducted by the systemic preparation in management activity and a required cost. Therefore, the annual due for the longterm repair plan is important to prepare the repair cost in a required time. In this paper, it aimed at analyzing the longterm repair cost and modelling to forecast the required cost in total area, number of household and time elapse in apartment housing. The estimation model of a repair cost is used with a power function which has a good statistics. Results of this study are shown that the sample has a longterm repair due in a $2,032won/m^2{\cdot}yr$ averagely which is higher than $912won/m^2{\cdot}yr$ in domestic. Second, the longterm repair due is proportionally correlated with the time elapse in both a total area and the number of household. Third, the estimation model for the longterm repair amount is suitable for the power function which is most in any other estimation models. Fourth, the ration of the longterm plan repair due a year to the cumulated longterm amount is about 26%.

• The Korean Journal of Applied Statistics
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• v.20 no.3
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• pp.541-550
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• 2007

One of main aspects of time series analysis is to forecast future values of series based on values up to a given time. The prediction interval for future values is usually obtained under the normality assumption. When the assumption is seriously violated, a transformation of data may permit the valid use of the normal theory. We investigate the prediction problem for future values in the original scale when transformations are applied in ARMA models. In this paper, we introduce the methodology based on Yeo-Johnson transformation to solve the problem of skewed data whose modelling is relatively difficult in the analysis of time series. Simulation studies show that the coverage probabilities of proposed intervals are closer to the nominal level than those of usual intervals.