• Journal of Information Technology Services
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• v.18 no.5
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• pp.155-164
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• 2019

With the emergence of new ICT technologies, information security threats are becoming more advanced, intelligent, and diverse. Even though the awareness of the importance of information security increases, the information security budget is not enough because of the lack of effectiveness measurement of the information security investment. Therefore, it is necessary to optimize the information security investment in each business environment to minimize the cost of operating the information security countermeasures and mitigate the damages occurred from the information security breaches. In this paper, using genetic algorithms we propose an investment optimization model for information security countermeasures with the limited budget. The optimal information security countermeasures were derived based on the actual information security investment status of SMEs. The optimal solution supports the decision on the appropriate investment level for each information security countermeasures.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.8
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• pp.585-592
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• 1989

This paper presents an efficient algorithm for the reactive planning of transmission network under normal operating conditions. The optimal operation of a power system is a prerequisite to obtain the optimal investment planning. The operation problem is decomposed into a P-optimization module and a Q-optimization module, but both modules use the same objective function of generation cost. In the investment problem, a new variable decomposition technique is adopted which can operate the operation and the investment variables. The optimization problem is solved by using the gradient projection method (GPM).

• Knowledge Management Research
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• v.19 no.1
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• pp.97-118
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• 2018

The finance-investment industry is currently focusing on research related to artificial intelligence and big data, moving beyond conventional theories of financial engineering. However, the case of equity optimization portfolio by using an artificial intelligence, big data, and its performance is rarely realized in practice. Thus, the purpose of this study is to propose process improvements in equity selection, information analysis, and portfolio composition, and lastly an improvement in portfolio returns, with the case of an equity optimization model based on quantitative research by an artificial intelligence. This paper is an empirical study of the portfolio based on an artificial intelligence technology of "D" asset management, which is the largest domestic active-quant-fiduciary management in accordance with the purpose of this paper. This study will apply artificial intelligence to finance, analyzing financial and demand-supply information and automating factor-selection and weight of equity through machine learning based on the artificial neural network. Also, the learning the process for the composition of portfolio optimization and its performance by applying genetic algorithms to models will be documented. This study posits a model that the asset management industry can achieve, with continuous and stable excess performance, low costs and high efficiency in the process of investment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.678-691
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• 2015

Internet and mobile traffic continues to surge exponentially in recent years due to popularization of smart devices, the appearance of various internet services carrying large amount of traffic from richer content and applications. This phenomenon leaded to various network problems such as the congestion delay, the non-balanced traffic ratio between ISPs, the continuous network investment cost and the Internet access problems. In light of changed data-driven communication ecosystem, There are growing concerns by both academia and industry that settlement-free peering and full transit regime have the limitations such as not only difficulties in maintaining mutual benefits but also difficulties in securing investment incentives for upgrading network performance and quality. Thus, it becomes more necessary for introducing the evolved internet interconnection regime which can fulfill the All-IP network environment. This study derives core issues regarding internet interconnection regime in Korea and suggest new evolved alternatives based on three point of view(traffic optimization, cost optimization, network investment optimization) through the empirical analysis.

• Journal of KIEE
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• v.11 no.1
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• pp.51-54
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• 2001

In this paper, a MW-Mvar investment technique focused on minimizing the system loss is presented. An optimization technique, in which the system loss is defined as the objective function and the power flow equations as the constraints, is introduced to obtain the Lagrangian multipliers λP and λQ. The Lagrangian multipliers imply the variation of the system loss with respect to incremental bus power and are used as MW-Mvar investment indices for minimizing the system loss. ΔP MW and ΔQ Mvar are invested, step by step, by the priority of λP and λQ index given for each bus. Derivation of the index uses the information from normal power flow calculation.

• Korean Management Science Review
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• v.29 no.1
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• pp.57-71
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• 2012

In this paper, we deal with a sector investment strategy by implementing the black-litterman model that incorporates expert evaluation and sector rotation momentum. Expert evaluation analyzes the relative performance of the industry sector compared with the market, while sector rotation momentum reflects the price impact of significant sector anomaly. In addition, we consider the portfolio impact of sector cardinality and weight constraints within the context of mean-variance portfolio optimization. Finally, we demonstrate the empirical viability of the proposed sector investment strategy with KOSPI 200 data.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1481-1500
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• 2017

This study examines the stability and excellence of portfolio investment strategies based on the accounting information of the Korean stock market. In the process of constructing the portfolio, various combinations of financial ratios are used to select the stocks with high expected return and to measure their performance. We also tried to improve our investment performance by using genetic algorithm optimization. The results of this study show that portfolio strategies using accounting information are effective for investment decision making and can achieve high investment performance. We also verify that portfolio strategy using genetic algorithms can be effective for investment decision making.

• Journal of the Korea Society for Simulation
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• v.26 no.3
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• pp.105-113
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• 2017

This study considers an optimal investment planning for improving survivability from an air threat in the layered air defense system. To establish an optimization model, we first represent the layered air defense system as a network model, and then, present two optimization models minimizing the failure probability of counteracting an air threat subject to budget limitation, in which one deals with whether to invest and the other enables continuous investment on the subset of nodes. Nonlinear objective functions are linearized using log function, and we suggest dynamic programming algorithm and linear programing for solving the proposed models. After designing a layered air defense system based on a virtual scenario, we solve the two optimization problems and analyze the corresponding optimal solutions. This provides necessity and an approach for an effective investment planning of the layered air defense system.

• Management Science and Financial Engineering
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• v.20 no.2
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• pp.13-25
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• 2014

We investigate an optimal retirement time and consumption/investment policy of a wage earner who expects to find a better investment opportunity after retirement by being freed from other work and participating fully in the financial market. We obtain a closed form solution to the optimization problem by using a dynamic programming method under general time-separable von Neumann-Morgenstern utility. It is optimal for the wage earner to retire from work if and only if his wealth exceeds a certain critical level which is obtained from a free boundary value problem. The wage earner consumes less and takes more risk than he would without anticipation of a better investment opportunity.

• East Asian mathematical journal
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• v.33 no.1
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• pp.23-36
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• 2017

We study an optimization problem for HARA utility function under a multi-scale Heston's stochastic volatility model. We investigate a practical strategy that do not depend on the incorporated factor which is unobservable in the market.