• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.171-174
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• 2006

A large number of service providers in countries all over the world have established GNSS reference station networks in the last years and are using network software today to provide a correction stream to the user as a routine service. In current GNSS network processing, all the geometric related information such as ionospheric free carrier phase ambiguities from all stations and satellites, tropospheric effects, orbit errors, receiver and satellite clock errors are estimated in one centralized Kalman filter. Although this approach provides an optimal solution to the estimation problem, however, the processing time increases cubically with the number of reference stations in the network. Until now one single Personal Computer with Pentium 3.06 GHz CPU can only process data from a network consisting of no more than 50 stations in real time. In order to process data for larger networks in real time and to lower the computational load, a federated filter approach can be considered. The main benefit of this approach is that each local filter runs with reduced number of states and the computation time for the whole system increases only linearly with the number of local sensors, thus significantly reduces the computational load compared to the centralized filter approach. This paper presents the technical aspect and performance analysis of the federated filter approach. Test results show that for a network of 100 reference stations, with the centralized approach, the network processing including ionospheric modeling and network ambiguity fixing needs approximately 60 hours to process 24 hours network data in a 3.06 GHz computer, which means it is impossible to run this network in real time. With the federated filter approach, only less than 1 hour is needed, 66 times faster than the centralized filter approach. The availability and reliability of network processing remain at the same high level.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.10
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• pp.595-602
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• 2003

This Paper presents an efficient hybrid techniques for a standard cell placement. The prototype tool adopts a middle-down methodology in which an n${\times}$m grid is imposed over the layout area and cells are assigned to bins forming a global placement. The optimization technique applied in this phase is based on the Relaxation-Based Local Search (RBLS) framework [12］in which a combinatorial search mechanism is driven by an analytical engine. This enables a more global view of the problem and results in complex modifications of the placement in a single search“move.”Details of this approach including a novel placement legalization procedure are presented. When a global placement converges, a detailed placement is formed and further optimized by the optimal interleaving technique[13］. Experimental results on MCNC benchmarking circuits are presented and compared with the Feng Shui's results in［14］. Solution Qualifies are almost the same as the Feng Shui's results.

• Journal of The Korean Society of Emergency Medicine
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• v.29 no.5
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• pp.399-407
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• 2018

Objective: Regionalization is one of the principal subjects for the advancement of rural emergency medical service systems in South Korea. This study shows the characteristics of interhospital transfer and status of the incidence of three major emergency disorders (acute myocardial infarction [AMI], acute stroke, and severe trauma) in one local province. Methods: A retrospective study was conducted for patients with three major emergency disorders who visited emergency medical facilities in one local province from January 2013 to December 2015, on the basis of the National Emergency Department Information System (NEDIS) data. Results: The incidence of three major emergency disorders had increased annually. Patients with each of these disorders tended to choose distinguishing methods of visiting emergency medical facilities. AMI patients tended to visited emergency medical facilities using private cars or on foot, while severe trauma patients usually visited by 119 ambulance, and acute stroke patients used 119 ambulance and private car in similar amounts. Overall, 65% of AMI patients were treated in intraregional medical facilities, but about 70% of acute stoke and severe trauma patients were transferred outside of the region. Conclusion: Because each of these disorders has an individual characteristic, it is difficult to expect a solution for the problems associated with emergency disorders just by assuring the availability of medical resources. Based on regionalization, a policy to provide the optimal treatment for those emergency disorders should be developed by planning public medical service systems based on the individual characteristics of emergency disorders, the standardized transfer plans of emergency patients and the assurance for mobilization and sharing of confined medical resources.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.49-56
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• 2023

In this study, we propose a genetic algorithm (GA) to optimize the allocation and operation order of transporters. The solution in the GA is represented by a set of lists each of which the operation order of the corresponding transporter. In addition, it was implemented in the form of a hybrid genetic algorithm combining effective local search operations for performance improvement. The local search reduces the number of operating transporters by moving blocks from a transporter with a low workload into that with a high workload. To evaluate the effectiveness of the proposed algorithm, it was compared with Multi-Start and a pure genetic algorithm through a simulation environment similar in scale to an actual shipyard. For the largest problem, compared to them, the number of transporters was reduced by 40% and 34%, and the total task time was reduced by 27% and 17%, respectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.717-720
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• 2007

This paper presents a novel scheme that quickly searches for the optimal direction of multiple directional antennas, and locks on to it for high-speed millimeter wavelength transmissions, when communications to another antenna directional are disconnected. The proposed method utilizes a modified genetic algorithm, which selects a superior initial group through preprocessing in order to solve the local solution in genetic algorithm. TDD (Time Division Duplex) is utilized as the transfer method and data controller for the antenna. Once the initial communication is completed for the specific number of individuals, no longer antenna's data will be transmitted nil each station processes GA in order to produce the next generation. After reproduction, individuals of the next generation become the data, and communication between each station is made again. In order to verify the effectiveness of the proposed system, simulation results of 1:1, 1:2, 1:5 directional antennas and experiment results of 1:1 directionalantennas confirmed the efficiency of the proposed method. The 16bit split is 8bit, but it has similar performance as 16bit gene.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.514-522
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• 2017

Traffic offloading is a promising solution to solve the explosive growth of mobile traffic. One of offloading schemes, in LIPA/SIPTO(Local IP Access and Selected IP Traffic Offload) offloading, we can offload mobile traffic that can satisfy QoS requirement for application. In addition, it is necessary for traffic offloading using social context due to large traffic from SNS. Thus, we propose the LIPA/SIPTO offloading algorithm using social context. We define the application selection probability using social context, the application popularity. Then, we find the optimal offloading weighting factor to maximize the QoS(Quality of Service) of small cell users in term of effective data rate. Finally, we determine the offloading ratio by this application selection probability and optimal offloading weighting factor. By performance analysis, the effective data rate achievement ratio of the proposed algorithm is similar with the conventional one although the total offloading ratio of the proposed algorithm is about 46 percent of the conventional one.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1106-1110
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• 2010

This paper proposes a method of optimizing a stacking strategy for an automated container terminal using multi-objective evolutionary algorithms (MOEAs). Since the yard productivities of seaside and landside are conflicting objectives to be optimized, it is impossible to maximize them simultaneously. Therefore, we derive a Pareto optimal set instead of a single best solution using an MOEA. Preliminary experiments showed that the population is frequently stuck in local optima because of the difficulty of the given problem depending on the yard occupancy rate. To cope with this problem, we propose another method of simultaneously optimizing two problems with different difficulties so that diverse solutions can be preserved in the population. Experimental results showed the proposed method can derive better stacking policies than the compared method solving a single problem given the same computational costs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.12
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• pp.1102-1112
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• 2012

We propose a cell selection and resource allocation scheme that assigns users to nearby accessible cells in heterogeneous wireless networks consisting of macrocell, femtocells, and Wi-Fi access points, under overload situation. Given the current power level of all accessible cells nearby users, the proposed scheme finds all possible cell assignment mappings of which user should connect to which cell to maximize the number of users that the network can accommodate at the same time. We formulate the cell selection problem with heterogeneous cells into an optimization problem of binary integer programming, and compute the optimal solution. We evaluate the proposed algorithm in terms of network access failure compared to a local ad-hoc based cell selection scheme used in practical systems using network level simulations. We demonstrate that our cell selection algorithm dramatically reduces network access failure in overload situation by fully leveraging network resources evenly across heterogeneous networks. We also validate the practical feasibility in terms of computational complexity of our binary integer program by measuring the computation time with respect to the number of users.

• Smart Structures and Systems
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• v.15 no.5
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• pp.1373-1392
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• 2015

Placing sensors at appropriate locations is an important task in the design of an efficient structural health monitoring (SHM) system for a large-scale civil structure. In this paper, a hybrid optimization algorithm called virus monkey algorithm (VMA) based on the virus theory of evolution is proposed to seek the optimal placement of sensors. Firstly, the dual-structure coding method is adopted instead of binary coding method to code the solution. Then, the VMA is designed to incorporate two populations, a monkey population and a virus population, enabling the horizontal propagation between the monkey and virus individuals and the vertical inheritance of monkey's position information from the previous to following position. Correspondingly, the monkey population in this paper is divided into the superior and inferior monkey populations, and the virus population is divided into the serious and slight virus populations. The serious virus is used to infect the inferior monkey to make it escape from the local optima, while the slight virus is adopted to infect the superior monkey to let it find a better result in the nearby area. This kind of novel virus infection operator enables the coevolution of monkey and virus populations. Finally, the effectiveness of the proposed VMA is demonstrated by designing the sensor network of the Canton Tower, the tallest TV Tower in China. Results show that innovations in the VMA proposed in this paper can improve the convergence of algorithm compared with the original monkey algorithm (MA).

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.330-339
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• 2008

Ground-based interceptors(GBI) comprise a major element of the strategic defense against hostile targets like Intercontinental Ballistic Missiles(ICBM) and reentry vehicles(RV) dispersed from them. An optimum design of the subsystems is required to increase the performance and reliability of these GBI. Propulsion subsystem design and optimization is the motivation for this effort. This paper describes an effort in which an entire GBI missile system, including a multi-stage solid rocket booster, is considered simultaneously in a Genetic Algorithm(GA) performance optimization process. Single goal, constrained optimization is performed. For specified payload and miss distance, time of flight, the most important component in the optimization process is the booster, for its takeoff weight, time of flight, or a combination of the two. The GBI is assumed to be a multistage missile that uses target location data provided by two ground based RF radar sensors and two low earth orbit(LEO) IR sensors. 3Dimensional model is developed for a multistage target with a boost phase acceleration profile that depends on total mass, propellant mass and the specific impulse in the gravity field. The monostatic radar cross section (RCS) data of a three stage ICBM is used. For preliminary design, GBI is assumed to have a fixed initial position from the target launch point and zero launch delay. GBI carries the Kill Vehicle(KV) to an optimal position in space to allow it to complete the intercept. The objective is to design and optimize the propulsion system for the GBI that will fulfill mission requirements and objectives. The KV weight and volume requirements are specified in the problem definition before the optimization is computed. We have considered only continuous design variables, while considering discrete variables as input. Though the number of stages should also be one of the design variables, however, in this paper it is fixed as three. The elite solution from GA is passed on to(Sequential Quadratic Programming) SQP as near optimal guess. The SQP then performs local convergence to identify the minimum mass of the GBI. The performance of the three staged GBI is validated using a ballistic missile intercept scenario modeled in Matlab/SIMULINK.