• Journal of Internet Computing and Services
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• v.17 no.2
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• pp.77-85
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• 2016

Internet cookie technology is designed for solving unreliable problem of HTTP's inherent property and notifying user's previous activity to web site's server, so it is useful to provide suitable service for individual user. However, the cookie techniques are becoming more sophisticated such as the third cookie and super cookie. And its included information is applied for advertisement and target marketing strategy, so the problem occurs that user's personal information is collected excessively. However, our law does not recognize the internet cookie as personal information so user cannot know where own internet cookie is stored and applicable. Therefore, in this paper, we explain the internet cookie technology, the privacy invasion and right to be forgotten for solving problem due to the internet cookie. And we analysis the relationship between the information of internet cookie and personal information, and then present the improvement requirement on the law and technology to use internet cookie securely and conveniently.

• Journal of Korean Philosophical Society
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• v.144
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• pp.205-234
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• 2017

In this essay, I modify and improve an anger-solving approach based on Seneca's theory of anger. Seneca proposed his own theory on anger and its therapy in his book, De Ira. Jin-Nam Yi developed five-step-anger-solving approach based on Seneca's theory and John Dewey's problem-solving steps. I show that Yi's approach is restricted in preventive short-term therapy with a constricted concept of anger. I also propose that we should treat controllable anger on preventive levels and uncontrollable anger on therapeutic levels. Adding the sixth step, 'jumping' in order to treat counselees with uncontrollable angers, I suggest a new anger-solving approach with clinical examples.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.287-295
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• 2007

This paper proposes a novel data editing techniques with genetic algorithm (GA) in case-based reasoning (CBR) for the prediction of Korea Stock Price Index (KOSPI). CBR has been widely used in various areas because of its convenience and strength in compelax problem solving. Nonetheless, compared to other machine teaming techniques, CBR has been criticized because of its low prediction accuracy. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However. designing a good matching and retrieval mechanism for CBR system is still a controversial research issue. In this paper, the GA optimizes simultaneously feature weights and a selection task for relevant instances for achieving good matching and retrieval in a CBR system. This study applies the proposed model to stock market analysis. Experimental results show that the GA approach is a promising method for data editing in CBR.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.143-159
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• 2019

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.2
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• pp.43-63
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• 2013

Remanufacturing is a process of recovering end-of-life products into serviceable parts for producing new products. Due to the limited supply of recovery cores to remanufacture, a remanufacturing firm also needs to produce or procure new parts for fulfilling the demand. This paper is targeted for solving the problem of determining the optimal amount of newly produced and remanufacturing parts, which is called production and remanufacturing planning (PRP) problem, under uncertain supply of recovery cores. The new production mitigates the risk of insufficient core supply while it takes more costs than the remanufacturing. The PRP model in this paper also considers disassemble-to-order (DTO) environment, in which multiple kinds of parts are remanufactured from multiple products on order of the parts. Whereas existing studies presents only heuristic solutions for DTO remanufacturing, this paper provides an exact solution for this problem and analytical sensitivity of the involved cost parameters, adopting multi-dimensional newsvendor modeling and stochastic linear programming techniques. The result shows that production and remanufacturing plans for multiple products are mutually dependent, and a change of cost parameters involved in only one part is propagated to all other parts.

• Structural Engineering and Mechanics
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• v.28 no.5
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• pp.525-548
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• 2008

This article deals the theory for solving an inverse problem of plate structures using the frequency-domain information instead of classical time-domain delays or free vibration eigenmodes or eigenvalues. A reduced set of output parameters characterizing the defect is used as a regularization technique to drastically overcome noise problems that appear in imaging techniques. A deconvolution scheme from an undamaged specimen overrides uncertainties about the input signal and other coherent noises. This approach provides the advantage that it is not necessary to visually identify the portion of the signal that contains the information about the defect. The theoretical model for Quantitative nondestructive evaluation, the relationship between the real and ideal models, the finite element method (FEM) for the forward problem, and inverse procedure for detecting the defects are developed. The theoretical formulation is experimentally verified using dynamic responses of a steel plate under impact loading at several points. The signal synthesized by FEM, the residual, and its components are analyzed for different choices of time window. The noise effects are taken into account in the inversion strategy by designing a filter for the cost functional to be minimized. The technique is focused toward a exible and rapid inspection of large areas, by recovering the position of the defect by means of a single accelerometer, overriding experimental calibration, and using a reduced number of impact events.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.943-952
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• 2000

The surface reconstruction problem from a set of wire-frame contours is very important in diverse fields such as medical imaging or computer animation. In this paper, surface triangulation method is proposed for solving the problem. Generally, many optimal triangulation techniques suffer from the large computation time but heuristic approaches may produce very unnatural surface when contours are widely different in shape. To compensate the disadvantages of these approaches, we propose a new heuristic triangulation method which iteratively decomposes the surface generation problem from a band (a pair of vertices chain) into tow subproblems from two sub-bands. Generally, conventional greedy heuristic contour triangulation algorithm, suffer from the drastic error propagation during surface modeling when the adjacent contours are different in shape. Our divide-and-conquer algorithm, called band partitioning algorithm, processes eccentric parts of the contours first with more global information. Consequently, the resulting facet model becomes more stable and natural even though the shapes are widely different. An interesting property of our method is hat it supports multi-resolution capability in surface modeling time. According to experiments, it is proved to be very robust and efficient in many applications.

• The Transactions of the Korea Information Processing Society
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• v.3 no.3
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• pp.564-571
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• 1996

The logic synthesis systems for table look up(TLU) type field programmable e gate arrays(FPGAs) have so farstudied mostly the combinational logic problem m. This paper presents for mapping a sequential circuit onto a popular table look up architecture, theXilinx 3090 architecture. In thefirst for solving this problem, combinational and sequential elements which have 6 or7 input combinational and sequential elements which haveless thanor equal to 5 inputs. We heavily use the combinational synthesis techniques tosolve the sequential synthesis problem. Our syntheisis approach is very simple, but its results are reasonable. We compare seveal benchmark Examples with sis-pga(map_together and map_separate) synthesis system and the experimental results show that our synthesis system is 17% betterthan sis-pga sequential synthesis system for TLU PGAs.

• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.2
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• pp.187-200
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• 1998

This paper presents an original approach for solving short-term power scheduling in extended power system with two fuels in a unit and a limited fuel using Lagrangian relaxations. The underlying model incorporates the full set of costs and constraints including setup, production, ramping, and operational status, and takes the form of a mixed integer nonlinear control problem. Moreover, the mathematical model developed includes two fuels in a unit and a limited fuel, regulation reserve requirements of prespecified group of units. Lagrangian relaxation is used to disaggregate the model by generator into separate subproblems which are then solved with a nested dynamic program including empirical knowledges. The strength of the methodology lies partially in its ability to construct good feasible solutions from information provided by the dual. Thus, the need for branch-and-bound is eliminated. In addition, the inclusion of two fuels in a unit and a limited fuel provides new insight into the limitations of current techniques. Computational experience with the proposed algorithm indicates that Problems containing up to 23 units including 8 unit used two fuels and 24 time periods can be readily solved in reasonable times. Duality gaps of less than 4％ were achieved.

• Magazine of the Korean Society of Agricultural Engineers
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• v.29 no.4
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• pp.73-92
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• 1987

Formulation of the geometric optimization for truss structures based on the elasticity theory turn out to be the nonlinear programming problem which has to deal with the cross-sectional area of the member and the coordinates of its nodes simultaneously. A few techniques have been proposed and adopted for the analysis of this nonlinear programming problem for the time being. These techniques, however, bear some limitations on truss shapes, loading conditions and design criteria for the practical application to real structures. A generalized algorithm for the geometric optimization of the truss structures, which can eliminate the above mentioned limitations, is developed in this study. The algorithm proposed utilizes the two-levels technique. In the first level which consists of two phases, the cross-sectional area of the truss member is optimized by transforming the nonlinear problem into SUMT, and solving SUMT utilizing the modified Newton Raphson method. In the second level, which also consists of two phases the geometric shape is optimized utillzing the unindirectional search technique of the Powell method which make it possible to minimize only the objective functlon. The algorithm proposed in this study is numerically tested for several truss structures with various shapes, loading conditions and design criteria, and compared with the results of the other algorithms to examine its applicability and stability. The numerical comparisons show that the two- levels algorithm proposed in this study is safely applicable to any design criteria, and the convergency rate is relatively fast and stable compared with other iteration methods for the geometric optimization of truss structures. It was found for the result of the shape optimization in this study to be decreased greatly in the weight of truss structures in comparison with the shape optimization of the truss utilizing the algorithm proposed with the other area optimum method.