• The Journal of Korean Institute for Practical Engineering Education
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• v.3 no.2
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• pp.69-74
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• 2011

This paper proposes elementary Dolittle programming problems using the algorithmic thinking-based problem model with material factors in the elementary Dolittle programming. And this paper proves the validity of developed Dolittle programming problems in defining them as algorithmic thinking-based problems through experiments. The experimental results are analyzed in views of variety and effectiveness evaluation of answer algorithms and suitability of allocating degrees of difficulties to the developed Dolittle programming problems.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2313-2324
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• 1998

Although visual programming is used in many fields of computer science and engineering, some dis-advantages can be found when they work together in an integrated programming environment. To overcome these shortcomings, there have been researches in combining multiparadigm with visual programming. However they have failed because they tried to combine the paramdigms without any coceptural model and structured method. In this paper, we investigate a new multiparadigm visual programming environment (MVPE), in which dataflow paradigm, form-based paradign, direct manipulation paradigm, and object-oriented paradigm are integrated together in an object-oriented way, based on the conceptual model of "method = paradigm, " This MVPE would overcome the limits of visual programming, and may also lead to the new discipline of visual programming environment.vironment.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.780-785
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• 2010

This paper introduces GP(Genetic Programming) based color detection model for an object detection and tracking. Existing color detection methods have used linear/nonlinear transformatin of RGB color-model and improved color model for illumination variation by optimization or learning techniques. However, most of cases have difficulties to classify various of colors because of interference of among color channels and are not robust for illumination variation. To solve these problems, we propose illumination robust and non-parametric multi-colors detection model using evolution of GP. The proposed method is compared to the existing color-models for various colors and images with different lighting conditions.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.2
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• pp.74-85
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• 1997

This study aims at the development of a mathematical approach for the optimal water allocation in the river basin where available water is not in sufficient. Its optimal allocation model is determined from the comparison and analysis of mathematical programming techniques such as transportation programming and dynamic programming models at its optimal allocation models. The water allocation system used in this study is designed to be the optimal water allocation which can satisfy the water deficit in each district through inter-basin water transfer between Kumho river basin which is a tributary catchment of Nakdong river basin, and the adjacent Hyungsan river basin, Milyang river basin and Nakdong upstream river basin. A general rule of water allocation is obtained for each district in the basins as the result of analysis of the optimal water allocation in the water allocation system. Also a comparison of the developed models proves that there is no big difference between the models Therefore transportation programming model indicates most adequate to the complex water allocation system in terms of its characteristics It can be seen, however, that dynamic programming model shows water allocation effect which produces greater net benefit more or less.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.481-488
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• 2020

Symbolic regression is an analysis method that directly generates a function that can explain the relationsip between dependent and independent variables for a given data in regression analysis. Genetic Programming is the leading technology of research in this field. It has the advantage of being able to directly derive a model that can be interpreted compared to other regression analysis algorithms that seek to optimize parameters from a fixed model. In this study, we propse a symbolic regression algorithm using parallel genetic programming based on a coarse grained parallel model, and apply the proposed algorithm to PMLB data to analyze the effectiveness of the algorithm.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.39-54
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• 1997

This paper presents four process models for machining processes : 1) an economical mathematical model of machining process, 2) a prediction model for surface roughness, 3) a decision model for fuzzy cutting conditions, and 4) a judgment model of machinability with automatic selection of cutting conditions. Each model was developed the economic machining, and these models were applied to theories widely studied in industrial engineering which are nonlinear programming, computer simulation, fuzzy theory, and neural networks. The results of this paper emphasize the human oriented domain of a nonlinear programming problem. From a viewpoint of the decision maker, fuzzy nonlinear programming modeling seems to be apparently more flexible, more acceptable, and more reliable for uncertain, ill-defined, and vague problem situations.

• Journal of information and communication convergence engineering
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• v.6 no.4
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• pp.448-453
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• 2008

This study describes design and development techniques of estimation models for process modeling. One case study is undertaken to design a model using standard gas furnace data. Neural networks (NN) and genetic programming (GP) are each employed to model the crucial relationships between input factors and output responses. In the case study, two models were generated by using 70% training data and evaluated by using 30% testing data for genetic programming and neural network modeling. The model performance was compared by using RMSE values, which were calculated based on the model outputs. The average RMSE for training and testing were 0.8925 (training) and 0.9951 (testing) for the NN model, and 0.707227 (training) and 0.673150 (testing) for the GP model, respectively. As concern the results, the NN model has a strong advantage in model training (using the all data for training), and the GP model appears to have an advantage in model testing (using the separated data for training and testing). The performance reproducibility of the GP model is good, so this approach appears suitable for modeling physical fabrication processes.

• Journal of Korean Society of Transportation
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• v.23 no.7 s.85
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• pp.111-123
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• 2005

A network design model has been proposed. which represents a transportation facility investment decision problem. The model takes the discrete hi-level programming form in which two types of decision makers, government and travelers, are involved. The model is characterized by its ability to address the total social costs occurring in transportation networks and to estimate the equilibrium link volumes in multi-modal networks. Travel time and volume for each link in the multi-modal network are predicted by a joint modal split/traffic assignment model. An efficient solution algorithm has been developed and an illustrative example has been presented.

• Journal of the Society of Naval Architects of Korea
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• v.52 no.1
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• pp.1-7
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• 2015

The weight estimation of floating offshore structures such as FPSO, TLP, semi-Submersibles, Floating Offshore Wind Turbines etc. in the preliminary design, is one of direct measures of both construction cost and basic performance. Through both literature investigation and internet search, the weight data of floating offshore structures such as FPSO and TLP was collected. In this study, the weight estimation model with the genetic programming was suggested for FPSO. The weight estimation model using genetic programming was established by fixing the independent variables based on this data. In addition, the correlation analysis was performed to make up for the weak points of genetic programming; it is apt to induce over-fitting when the number of data is relatively smaller than that of independent variables. That is, by reducing the number of variables through the analysis of the correlation between the independent variables, the increasing effect in the number of weight data can be expected. The reliability of the developed weight estimation model was within 2% of error rate.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.12
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• pp.638-644
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• 2017

Optimal cooling operation algorithm was developed based on a simulation case of a single family house model equipped with renewable energy facility. EnergyPlus simulation results were used as virtual test data. The model contained three energy storage elements: thermal heat capacity of the living room, chilled water storage tank, and battery. Their charging and discharging schedules were optimized so that daily electricity bill became minimal. As an optimization tool, linear programming was considered because it was possible to obtain results in real time. For its adoption, EnergyPlus-based house model had to be linearly approximated. Results of this study revealed that dynamic cooling load of the living room could be approximated by a linear RC model. Scheduling based on the linear programming was then compared to that by a nonlinear optimization algorithm which was made using GenOpt developed by a national lab in USA. They showed quite similar performances. Therefore, linear programming can be a practical solution to optimal operation scheduling if linear dynamic models are tuned to simulate their real equivalents with reasonable accuracy.