• Annual Conference of KIPS
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• 2014.04a
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• pp.146-148
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• 2014

In LTE-Advanced (LTE-A) networks, Relay nodes (RN) are used to improve the system coverage. However, it also brings new kind of interference to users which reduces the system performance. In this paper, we use an optimization relay node deployment to reduce the interference as much as possible and resource allocation to improve the user throughput. Our simulation results show our method is able to improve the user SINR and throughput.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.31-39
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• 2011

For rapid formed the ad hoc wireless backbone network in disaster scene, It is necessary for real-time deployment scheme of wireless ad hoc relay devices by first responders without pre-planning. However, in order to realize this scheme, redundant deployment should be minimized, as well as optimal location of relay devices should be selected to expand communication coverage. Therefore, in this paper, we propose a new deployment scheme of relay devices to optimize communication coverage and then through simulations showed that improved performance of algorithm.

• Korean Management Science Review
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• v.13 no.3
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• pp.91-104
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• 1996

The basic idea of Quality Function Deployment(QFD) is to deploy the voice of customers into the final product through product planning, part planning, process planning, and manufacturing. In the product planning stage, which is the first stage of product development, customer attributes(CAs) are translated into engineering characteristics(ECs). Then, based on the relationship between CAs and ECs, the target values of ECs are determined. In the previous research, the process of analyzing these relationships is mostly subjective in nature. In this article, we formulate the process of determining the target values of ECs as an optimization model. That is, we first determine the relationship between CAs and ECs as cumulative logit models and construct constraints into which the company strategy as well as the needs of customers can be incorprated. Next, cost functions of ECs are developed, which are summed into an objective function. An algorithm to solve the formulated optimization problem is developed and illustrated with an example.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.201-219
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• 2014

This paper considers service deployment and priority optimization for multiple service-oriented applications sharing reusable services, which are deployed as multiple instances in the cloud. In order to handle variations in the workloads of the multiple applications, service instances of the individual reusable services are dynamically provisioned in the cloud. Also service priorities for each application in a particular reusable service are dynamically adjusted. In this paper, we propose an analytic performance model, based on a queueing network model, to predict the expected sojourn times of multiple service-oriented applications, given the number of service instances and priority disciplines in individual reusable services. We also propose a simple heuristic algorithm to search an optimal number of service instances in the cloud and service priority disciplines for each application in individual reusable services. A numerical example is also presented to demonstrate the applicability of the proposed performance model and algorithm to the proposed optimal decision problem.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.129-139
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• 2019

In order for a gun-launched guided projectile to glide to the maximum range, when to deploy the fin and start flight with guidance and control should be considered in range optimization process. This study suggests a solution to the optimal guidance problem for flight range maximization of the flight model of a guided projectile in vertical plane considering the aerodynamic properties. After converting the nonlinear Multi-Phase Optimal Control Problem to Two-Point Boundary Value Problem, the optimized guidance command and the best fin deployment timing are calculated by the proposed numerical method. The optimization results of the multiple flight rounds with various initial velocity and launch angle indicate that determining specific launch condition incorporated with the guidance scheme is of importance in terms of mechanical energy consumption.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.409-416
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• 2009

This paper presents a tool called Quality-Cost optimization system (QCOS), which integrates Multi-Objective Genetic Algorithm (MOGA) and Quality Function Deployment (QFD), for tradeoff between quality and cost of the unitized metal curtain-wall unit. A construction owner as the external customer pursues to maximize the quality of the curtain-wall unit. However, the contractor as the internal customer pursues to minimize the cost involved in designing, manufacturing and installing the curtain-wall unit. It is crucial for project manager to find the tradeoff point which satisfies the conflicting interests pursued by the both parties. The system would be beneficial to establish a quality plan satisfying the both parties. Survey questionnaires were administered to the construction owner who has an experience of curtain-wall project, the architects who are the independent assessor, and the contractors who were involved in curtain-wall design and installation. The Customer Requirements (CRs) and their importance weights, the relationship between CRs and Technical Attributes (TAs) consisting of a curtain-wall unit, and the cost ratios of each components consisting curtain-wall unit are obtained from the three groups mentioned previously. The data obtained from the surveys were used as the QFD input to compute the Owner Satisfaction (OS) and Contractor Satisfaction (CS). MOGA is applied to optimize resource allocation under limited budget when multi-objectives, OS and CS, are pursued at the same time. The deterministic multi-objective optimization method using MOGA and QFD is extended to stochastic model to better deal with the uncertainties of QFD input and the variability of QFD output. A case study demonstrates the system and verifies the system conformance.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.98-104
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• 2016

With the development of modern science and technology, weapon systems such as tanks, submarines, combat planes, radar are also dramatically advanced. Among these weapon systems, the ballistic missile, one of the asymmetric forces, could be considered as a very economical means to attack the core facilities of the other country in order to achieve the strategic goals of the country during the war. Because of the current ballistic missile threat from the North Korea, establishing a missile defense (MD) system becomes one of the major national defense issues. This study focused on the optimization of air defense artillery units' deployment for effective ballistic missile defense. To optimize the deployment of the units, firstly this study examined the possibility of defense, according to the presence of orbital coordinates of ballistic missiles in the limited defense range of air defense artillery units. This constraint on the defense range is originated from the characteristics of anti-ballistic missiles (ABMs) such as PATRIOT. Secondly, this study proposed the optimized mathematical model considering the total covering problem of binary integer programming, as an optimal deployment of air defense artillery units for defending every core defense facility with the least number of such units. Finally, numerical experiments were conducted to show how the suggested approach works. Assuming the current state of the Korean peninsula, the study arbitrarily set ballistic missile bases of the North Korea and core defense facilities of the South Korea. Under these conditions, numerical experiments were executed by utilizing MATLAB R2010a of the MathWorks, Inc.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.214-224
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• 2024

Sonobuoys are disposable devices that utilize sound waves for information gathering, detecting engine noises, and capturing various acoustic characteristics. They play a crucial role in accurately detecting underwater targets, making them effective detection systems in anti-submarine warfare. Existing sonobuoy deployment methods in multistatic systems often rely on fixed patterns or heuristic-based rules, lacking efficiency in terms of the number of sonobuoys deployed and operational time due to the unpredictable mobility of the underwater targets. Thus, this paper proposes an optimal sonobuoy placement strategy for Unmanned Aerial Vehicles (UAVs) to overcome the limitations of conventional sonobuoy deployment methods. The proposed approach utilizes reinforcement learning in a simulation-based experimental environment that considers the movements of the underwater targets. The Unity ML-Agents framework is employed, and the Proximal Policy Optimization (PPO) algorithm is utilized for UAV learning in a virtual operational environment with real-time interactions. The reward function is designed to consider the number of sonobuoys deployed and the cost associated with sound sources and receivers, enabling effective learning. The proposed reinforcement learning-based deployment strategy compared to the conventional sonobuoy deployment methods in the same experimental environment demonstrates superior performance in terms of detection success rate, deployed sonobuoy count, and operational time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.973-980
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• 2009

Many studies which have been done for efficient installation and management of wireless sensor networks (WSN) include energy savings, key managements and sensor deployments. Sensor deployment problem is one of the most important and fundamental issues among them in that the topic is directly related with the system cost and performance. In this paper, we suggest a sensor deployment scheme that reduces the system cost of WSN while satisfying the fundamental system requirements of connectivity between sensor nodes and sensing coverage. Using graphical information system(GIS) which contains region-dependent information related with connectivity condition, the initial positions of sensors in the procedure simulated annealing (SA) are determined. The GIS information helps in reducing system cost reduction not only at the initial deployment of SA but also at the final deployment of SA which is shown by computer simulations.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.312-320
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• 2021

Increase in computational cost and exhaustive search can lead to more complexity and computational energy. Thus, there is need for effective and efficient scheme to reduce the complexity to achieve optimal energy utilization. This will improve the energy efficiency and enhance the proficiency in terms of the resources needed to achieve convergence. This paper primarily focuses on the development of hybrid swarm intelligence scheme for reducing the computational complexity in binary optimization. In order to reduce the complexity, both artificial bee colony (ABC) and particle swarm optimization (PSO) have been employed to effectively minimize the exhaustive search and increase convergence. First, a new approach using ABC and PSO has been proposed and developed to solve the binary optimization problem. Second, the scout for good quality food sources is accomplished through the deployment of PSO in order to optimally search and explore the best source. Extensive experimental simulations conducted have demonstrate that the proposed scheme outperforms the ABC approaches for reducing complexity and energy consumption in terms of convergence, search and error minimization performance measures.