• Journal of Korean Institute of Industrial Engineers
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• v.38 no.2
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• pp.144-156
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• 2012

In recent years, advanced countries in energy sector are emphasizing the importance of the development and deployment of renewable energy to cope with the global environmental crisis such as depletion of fossil energy, climate convention to control emissions of greenhouse gases. In this paper, we evaluate the economic value of the investment in new and renewable energy R&D in Korea and optimal deployment timing of new and renewable energy by using the real option approach. The real option model adopted in this paper assumes that a decision maker has a compound option to abandon, deployment, or continue the R&D. As a result by using empirical data of Korea, it is found that there exists a considerable amount of positive real option value (ROV) in the investment of new and renewable energy R&D while its net present value (NPV) calculated by traditional discounted cash flow (DCF) model shows negative value.

• Journal of Auto-vehicle Safety Association
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• v.13 no.3
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• pp.60-68
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• 2021

Invisible passenger airbag door system of hard panel types must be designed with a weakened area such that the side airbag will deploy through the instrument panel as like intended manner, with no flying debris at any required operating temperature. At the same time, there must be no cracking or sharp edges in the head impact test. If the advanced airbag with the big difference between high and low deployment pressure ranges are applied to hard panel types of invisible passenger airbag (IPAB) door system, it becomes more difficult to optimize the tearseam strength for satisfying deployment and head impact performance simultaneously. It was introduced the 'Operating Window' idea from quality engineering to design the hard panel types of IPAB door system applied to the advanced airbag for optimal deployment and head impact performance. Zigzab airbag folding and 'n' type PAB mounting bracket were selected.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1568-1589
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• 2021

Deployment of access point (AP) is a problem that must be considered in network planning. However, this problem is usually a NP-hard problem which is difficult to directly reach optimal solution. Thus, improved AP deployment optimization scheme based on swarm intelligence algorithm is proposed to research on this problem. First, the scheme estimates the number of APs. Second, the multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the location and transmit power of APs. Finally, the greedy algorithm is used to remove the redundant APs. Comparing with multi-objective whale swarm optimization algorithm (MOWOA), particle swarm optimization (PSO) and grey wolf optimization (GWO), the proposed deployment scheme can reduce AP's transmit power and improves energy efficiency under different numbers of users. From the experimental results, the proposed deployment scheme can reduce transmit power about 2%-7% and increase energy efficiency about 2%-25%, comparing with MOWOA. In addition, the proposed deployment scheme can reduce transmit power at most 50% and increase energy efficiency at most 200%, comparing with PSO and GWO.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4C
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• pp.338-346
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• 2012

Wireless sensor network (WSN) technology provides a variety of medical and healthcare solutions to assist detection and communication of body conditions. However, data reliability inside WSN might be influenced to healthcare routing protocol due to limited hardware resources of computer, storage, and communication bandwidth. For this reason, we have conducted various wireless communication experiments between nodes using parameters such as RF strength, battery status, and deployment status to get a optimal performance of mobile healthcare routing protocol. This experiment may also extend the life time of the nodes. Performance analysis is done to obtain some important parameters in terms of distance and reception rate between the nodes. Our experiment results show optimal distance between nodes according to battery status and RF strength, or deployment status and RF strength. The packet reception rate according to deployment status and RF strength of nodes was also checked. Based on this performance evaluation, the optimized sensor node battery and deployment in the developed our mobile healthcare routing protocol were proposed.

• 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 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.

• Journal of IKEEE
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• v.16 no.4
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• pp.316-321
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• 2012

In this paper, we study the energy-efficient deployment strategy for micro base station (BS) in wireless cellular systems. Firstly, we formulate a general problem pertaining to total energy consumption minimization with the requirement of area spectral efficiency (ASE). We start from an observation about the correlation between the area covered by an additional micro BS and the increment of ASE. Under such an observation, we propose an efficient greedy micro BS deployment algorithm. Simulations show that the proposed deployment algorithm can deploy micro BSs with a slight performance reduction comparing with the optimal solution.

• Smart Structures and Systems
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• v.17 no.3
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• pp.523-539
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• 2016

The ambient vibration measurement is an output-data-only dynamic testing where natural excitations are represented, for instance, by winds and typhoons. The modal identification involving output-only measurements requires the use of specific modal identification techniques. This paper presents the application of a reliable method (the Stochastic Subspace Identification - SSI) implemented in a general purpose software. As a criterion toward the robustness of identified modes, a bio-inspired optimization algorithm, with a highly nonlinear objective function, is introduced in order to find the optimal deployment of a reduced number of sensors across a large civil engineering structure for the validation of its modal identification. The Ting Kau Bridge (TKB), one of the longest cable-stayed bridges situated in Hong Kong, is chosen as a case study. The results show that the proposed method catches eigenvalues and eigenvectors even for a reduced number of sensors, without any significant loss of accuracy.

• Ocean and Polar Research
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• v.37 no.3
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• pp.179-188
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• 2015

This study was carried out to determine the optimal number of ARGO floats in the East Sea in order to maximize their applications. The dominant spatio-temporal scale, size of the domain, and the typical float lifetimes in the East Sea were taken into consideration. The mean spatial de-correlation scale of temperature on isobaric surfaces reaches about 60 km. The minimum necessary number of floats is about 82 on average in order to secure independent ARGO profiles with the de-correlation scale. Considering the float lifetimes, about 27 floats per year should be deployed to maintain the 82 ARGO float array every year. To obtain spatially uniform distribution of ARGO float data, mean residence time and dispersion rate (basin area/residence time) of ARGO floats were evaluated in each basin of the East Sea. A faster (slower) dispersion rate requires more (less) ARGO floats to maintain the spatially uniform number of floats. According to the analysis, it is likely that the optimal ratio of the number of floats for each basin is 1:2:4 corresponding to Ulleung Basin:Yamato Basin:Japan Basin. In order to maintain relatively uniform ARGO observing networks, it is necessary to establish a long-term plan for deployment strategy based on float pathways and the dispersion rate parameters estimated by using currently active ARGO float trajectory data as well as reanalysis data.

• Journal of the Korean Operations Research and Management Science Society
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• v.6 no.1
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• pp.75-82
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• 1981

This paper considers urban emergency service systems that dispatch vehicles (facilities) from fixed bases with the objective of finding an optimum allocation of emergency facilities. A case study of fire station problem of Seoul city is conducted to analyze the deployment of fire-fighting resources and develop a class of improved deployment strategies with Parameteric Allocation Model (P.A.M.). The study shows a long term plan leading to balanced fire protection for most fire hazardous districts as well as low alarm districts.