• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.184-193
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• 2015

Passive Surveillance System based on the TDOA detects the emitter position in the air using TOA of pulses comprising emitter signal from multiple receivers. In case that PRI of pulses from the emitter is not enough big in comparison with the distance among receivers, it causes the ambiguity problem in selecting proper pulse pairs for TDOA emitter geolocation. In this paper, a novel ambiguity resolution method of radar pulses is presented by using genetic algorithm after changing ambiguity problem into optimization problem between TDOA of received pulses from each receiver and emitter position. Simulation results are presented to show the performance of the proposed method.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.11
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• pp.291-296
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• 2021

The development of the Internet of Things(IoT) requires large computational resources for tasks from numerous devices. Mobile Edge Computing(MEC) has attracted a lot of attention in the IoT environment because it provides computational resources geographically close to the devices. Task offloading to MEC servers is efficient for devices with limited battery life and computational capability. In this paper, we assumed an industrial IoT environment requiring high reliability. The complexity of optimization problem in industrial IoT environment with many devices and multiple MEC servers is very high. To solve this problem, the problem is divided into two. After selecting the MEC server considering the queue status of the MEC server, we propose an offloading decision algorithm that optimizes reliability and energy consumption using genetic algorithm. Through experiments, we analyze the performance of the proposed algorithm in terms of energy consumption and reliability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3565-3583
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• 2022

The task scheduling problem has received a lot of attention in recent years as a crucial area for research in the cloud environment. However, due to the difference in objectives considered by service providers and users, it has become a major challenge to resolve the conflicting interests of service providers and users while both can still take into account their respective objectives. Therefore, the task scheduling problem as a bi-objective game problem is formulated first, and then a task scheduling model based on the bi-objective game (TSBOG) is constructed. In this model, energy consumption and resource utilization, which are of concern to the service provider, and cost and task completion rate, which are of concern to the user, are calculated simultaneously. Furthermore, a many-objective evolutionary algorithm based on a partitioned collaborative selection strategy (MaOEA-PCS) has been developed to solve the TSBOG. The MaOEA-PCS can find a balance between population convergence and diversity by partitioning the objective space and selecting the best converging individuals from each region into the next generation. To balance the players' multiple objectives, a crossover and mutation operator based on dynamic games is proposed and applied to MaPEA-PCS as a player's strategy update mechanism. Finally, through a series of experiments, not only the effectiveness of the model compared to a normal many-objective model is demonstrated, but also the performance of MaOEA-PCS and the validity of DGame.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1859-1864
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• 2004

An effective methodology is reported for the optimal design of multisite batch production/transportation and storage networks under uncertain demand forecasting. We assume that any given storage unit can store one material type which can be purchased from suppliers, internally produced, internally consumed, transported to or from other plant sites and/or sold to customers. We further assume that a storage unit is connected to all processing and transportation stages that consume/produce or move the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. A batch transportation process can transfer one material or multiple materials at once between plant sites. The objective for optimization is to minimize the probability averaged total cost composed of raw material procurement, processing setup, transportation setup and inventory holding costs as well as the capital costs of processing stages and storage units. A novel production and inventory analysis formulation, the PSW(Periodic Square Wave) model, provides useful expressions for the upper/lower bounds and average level of the storage inventory. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two sub-problems. The first yields analytical solutions for determining lot sizes while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks for the given demand forecast scenario. The result of this study will contribute to the optimal design and operation of large-scale supply chain system.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.389-394
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• 2008

The purpose of this research was to develop a methodology to determine whether conjunctive surface water and groundwater management could significantly reduce deficits in a river basin with a relatively limited alluvial aquifer. The Geum River basin is one of major river basins in South Korea. The upper region of the Geum River basin is typical of many river basins in Korea where the shape of river basin is narrow with small alluvial aquifer depths from 10m to 20m and where most of the groundwater pumped comes quickly from the steamflow. The basin has two surface reservoirs, Daecheong and Yongdam. The most recent reservoir, Yongdam, provides water to a trans-basin diversion, and therefore reduces the water resources available in the Geum River basin. After the completion of Yongdam reservoir, the reduced water supply in the Geum basin resulted in increasing conflicts between downstream water needs and required instream flows, particularly during the low flow season. Historically, the operation of groundwater pumping has had limited control and is administered separately from surface water diversions. Given the limited size of the alluvial aquifer, it is apparent that groundwater pumping is essentially taking its water from the stream. Therefore, the operation of the surface water withdrawals and groundwater pumping must be considered together. The major component of the conjunction water management in this study is a goal-programmin g based optimization model that simultaneously considers surface water withdrawals, groundwater pumping and instream flow requirements. A 10-day time step is used in the model. The interactions between groundwater pumping and the stream are handled through the use of response and lag coefficients. The impacts of pumping on streamflow are considered for multiple time periods. The model is formulated as a linear goal-programming problem that is solved with the commercial LINGO optimization software package.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.8
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• pp.758-765
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• 2016

This paper proposes a novel method for the recognition of the inverse synthetic aperture radar(ISAR) image of multiple targets flying in formation. Rather than separating the ISAR image of each target, the proposed method combines an ISAR image obtained by fusing the ISAR images in the training database. Fusion is conducted by optimizing the non-linear problem whose parameters are the aspect angle and the target location. Assuming that the aspect angle is properly estimated, the proposed method estimates the number of the targets and their locations by optimizing the template matching using PSO. In simulations using the F-16 scale model, the efficiency of the proposed method was demonstrated by yielding the ISAR image identical to that of targets in formation.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.6
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• pp.537-544
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• 2004

An effective methodology is reported for determining the optimal lot size of batch processing and storage networks which include uncertain demand forecasting. We assume that any given storage unit can store one material type which can be purchased from suppliers, internally produced, infernally consumed, transported to or from other sites and/or sold to customers. We further assume that a storage unit is connected to all processing and transportation stages that consume/produce or move the material to which that storage unit is dedicated. Each processing stage transforms a set of feedstock materials or intermediates into a set of products with constant conversion factors. A batch transportation process can transfer one material or multiple materials at once between sites. The objective for optimization is to minimize the probability averaged total cost composed of raw material procurement, processing setup, transportation setup and inventory holding costs as well as the capital costs of processing stages and storage units. A novel production and inventory analysis formulation, the PSW(Periodic Square Wave) model, provides useful expressions for the upper/lower bounds and average level of the storage inventory. The expressions for the Kuhn-Tucker conditions of the optimization problem can be reduced to two sub-problems. The first yields analytical solutions for determining lot sires while the second is a separable concave minimization network flow subproblem whose solution yields the average material flow rates through the networks for the given demand forecast scenario. The result of this study will contribute to the optimal design and operation of the global supply chain.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1204-1227
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• 2020

The tradeoff between energy conservation and traffic balancing is a dilemma problem in Wireless Sensor Networks (WSNs). By analyzing the intrinsic relationship between cluster properties and long distance transmission energy consumption, we characterize three node sets of the cluster as a theoretical foundation to enhance high performance of WSNs, and propose optimal solutions by introducing rendezvous and Mobile Elements (MEs) to optimize energy consumption for prolonging the lifetime of WSNs. First, we exploit an approximate method based on the transmission distance from the different node to an ME to select suboptimal Rendezvous Point (RP) on the trajectory for ME to collect data. Then, we define data transmission routing sequence and model rendezvous planning for the cluster. In order to achieve optimization of energy consumption, we specifically apply the economic theory called Diminishing Marginal Utility Rule (DMUR) and create the utility function with regard to energy to develop an adaptive energy consumption optimization framework to achieve energy efficiency for data collection. At last, Rendezvous Transmission Algorithm (RTA) is proposed to better tradeoff between energy conservation and traffic balancing. Furthermore, via collaborations among multiple MEs, we design Two-Orbit Back-Propagation Algorithm (TOBPA) which concurrently handles load imbalance phenomenon to improve the efficiency of data collection. The simulation results show that our solutions can improve energy efficiency of the whole network and reduce the energy consumption of sensor nodes, which in turn prolong the lifetime of WSNs.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.6
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• pp.669-676
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• 2008

The adaptive resource optimization problem in multi-input multi-output orthogonal frequency division multiple access (MIMO-OFDMA) systems is addressed. This paper, adaptive modulation and coding(AMC) and power control algorithms is proposed with SFC(Space-Frequency Coding), which aims to maximize the system capacity based on the CQI(Channel Quality Information). Firstly, power level is decided to each sub-channels with received feedback signal to noise ratio(SNR). In the second step, sub-carriers are allocated according to modulation type. Simulation results show that the proposed algorithm achieves a better performance than conventional algorithm in terms of capacity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.1-19
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• 2020

In an underlay cognitive simultaneous wireless information and power transfer (SWIPT) network, communication from secondary user (SU) to secondary destination (SD) is accomplished with decode-and-forward (DF) relays. Multiple energy-constrained relays are assumed to harvest energy from SU via power splitting (PS) protocol and complete SU secure information transmission with beamforming. Hence, physical layer security (PLS) is investigated in cognitive SWIPT network. In order to interfere with eavesdropper and improve relay's energy efficiency, a destination-assisted jamming scheme is proposed. Namely, SD transmits artificial noise (AN) to interfere with eavesdropping, while jamming signal can also provide harvested energy to relays. Beamforming vector and power splitting ratio are jointly optimized with the objective of SU secrecy capacity maximization. We solve this non-convex optimization problem via a general two-stage procedure. Firstly, we obtain the optimal beamforming vector through semi-definite relaxation (SDR) method with a fixed power splitting ratio. Secondly, the best power splitting ratio can be obtained by one-dimensional search. We provide simulation results to verify the proposed solution. Simulation results show that the scheme achieves the maximum SD secrecy rate with appropriate selection of power splitting ratio, and the proposed scheme guarantees security in cognitive SWIPT networks.