• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.5
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• pp.1085-1092
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• 2010

This paper describes the development of estimating equation for under 16 aged children's exposure to habitat's magnetic field for 24 hours by using particle swarm optimization(PSO) algorithm, which was carried out by using the measured database collected from the exposure survey to Korean habitat's magnetic field as to under 16 aged Korean students such as preschooler, children in elementary school, and children in middle school. Sex, age, residence type, size of habitation site, distance from power line, and power transmission voltage are used as the input data of estimating 24 hour's personal exposure to magnetic field. And distribution of 24 hour's personal exposure to magnetic field, exposure characteristic to magnetic field, and exposure characteristic to magnetic field according to special conditions, are analyzed for under 16 aged children.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1312-1320
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• 2009

This paper presents an efficient design method of 2-D infinite impulse response(IIR) digital filter based on a particle swarm optimization(PSO) algorithm. The design task is reformulated as a constrained minimization problem and is solved by our newly developed PSO algorithm. To ensure the stability of the designed 2-D IIR digital filters, a new stability strategy is embedded in the basic PSO algorithm. The superiority of the proposed method is demonstrated by several experiments. The results show that the approximation error of the resultant filters are better than those of the digital filters which designed by recently published filter design methods. The proposed design method can also obtain the stable2-D IIR digital filters.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.7
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• pp.602-609
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• 2018

This paper proposes the hybrid crypto-system for fast video data encryption of UAV(Unmanned Aerial Vehicle) under the LTE(Long-Term Evolution) wireless communication environment. This hybrid crypto-system is consisted of ECC(Elliptic Curve Cryptography) public key algorithm and LEA(Light-weight Encryption Algorithm) symmetric key algorithm. ECC is a faster public key algorithm with the same security strength than RSA(Rivest Shamir Adleman), and Korean standard LEA with the same key size is also a faster symmetric key algorithm than AES(Advances Encryption Standard). We have implemented this hybrid crypto-system using OpenSSL, OpenCV and Socket programs under the Swarm 8-UAV. We have shown the efficient adaptability of this hybrid crypto-system for the real-time swarm UAV through the experiments under the LTE communication environment.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.5
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• pp.458-464
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• 2016

This paper proposes the compensation method which decreases the radiation pattern distortion caused by the mutual coupling in an array antenna. If the element distance of an array antenna decreases, the radiation pattern could be distorted by the strong mutual coupling, which changes the magnitude and phase of input signals and causes an unwanted radiation pattern. To remove the pattern distortion, compensated input signals are inserted in an array antenna. The magnitude and phase of input signals are determined by Particle Swarm Optimization (PSO) algorithm. A $4{\times}1$ dipole array antenna with omnidirectional elements is used to confirm the validity of the algorithm, where each element is placed in 0.2 wavelength to evoke the strong coupling. After input signals are optimized by PSO, it is found that the compensated radiation results in the same as the ideal case.

• Journal of Advanced Navigation Technology
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• v.21 no.5
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• pp.443-449
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• 2017

Swarm of low-cost UAVs for search mission has benefit in the sense of rapid search compared to use of single high-end UAV. As the number of UAVs forming swarm increases, not only the time for the mission planning increases, but also the system to operate UAVs has excessive burden. This paper addresses a decentralized area search algorithm adequate for multiple UAVs which takes advantages of flexibility, robustness, and simplicity. To down the cost, it is assumed that each UAV has limited ability: close-communication, basic calculation, and limited memory. In close-communication, heath conditions and search information are shared. And collision avoidance and consensus of next search direction are then done. To increase weight on un-searched area and to provide overlapped search, the score function is introduced. Performance and operational characteristics of the proposed search algorithm and mission planning logic are verified via numerical simulations.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.367-379
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• 2022

In this paper, a multilayer radar absorbing structure was designed using the Particle Swarm Optimization (PSO) algorithm, and the characteristics of the multilayer radar absorbing structure were analyzed. It was shown that design values can be derived quickly and accurately by applying PSO to the design of a multilayer radar absorbing structure, and it is also shown that the optimal multilayer radar absorbing structure can be designed especially for an oblique incident. In addition, it was shown that the optimal value that meets the performance requirements can be determined even in a combination of various design parameters. It is presented through a comprehensive flowchart including the equations and detailed descriptions of all variables for each step. From the results of this paper, it is possible to omit complex and many calculations for designing a multilayer radar absorbing structure, and it is possible to use various composite materials. It can be utilized in the design and development of multilayer radar absorbing structures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2140-2156
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• 2023

The Wireless Sensor Network (WSN), is constructed out of teeny-tiny sensor nodes that are very low-cost, have a low impact on the environment in terms of the amount of power they consume, and are able to successfully transmit data to the base station. The primary challenges that are presented by WSN are those that are posed by the distance between nodes, the amount of energy that is consumed, and the delay in time. The sensor node's source of power supply is a battery, and this particular battery is not capable of being recharged. In this scenario, the amount of energy that is consumed rises in direct proportion to the distance that separates the nodes. Here, we present a Hybrid Firefly Glow-Worm Swarm Optimization (HF-GSO) guided routing strategy for preserving WSNs' low power footprint. An efficient fitness function based on firefly optimization is used to select the Cluster Head (CH) in this procedure. It aids in minimising power consumption and the occurrence of dead sensor nodes. After a cluster head (CH) has been chosen, the Glow-Worm Swarm Optimization (GSO) algorithm is used to figure out the best path for sending data to the sink node. Power consumption, throughput, packet delivery ratio, and network lifetime are just some of the metrics measured and compared between the proposed method and methods that are conceptually similar to those already in use. Simulation results showed that the proposed method significantly reduced energy consumption compared to the state-of-the-art methods, while simultaneously increasing the number of functioning sensor nodes by 2.4%. Proposed method produces superior outcomes compared to alternative optimization-based methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1585-1609
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• 2013

Over recent years, enormous amounts of research in wireless sensor networks (WSNs) have been conducted, due to its multifarious applications such as in environmental monitoring, object tracking, disaster management, manufacturing, monitoring and control. In some of WSN applications dependent the energy-efficient and link reliability are demanded. Hence, this paper presents a routing protocol that considers these two criteria. We propose a new mechanism called Reliable Routing Scheme for Energy-Balanced (RRSEB) to reduce the packets dropped during the data communications. It is based on Swarm Intelligence (SI) using the Ant Colony Optimization (ACO) method. The RRSEB is a self-adaptive method to ensure the high routing reliability in WSNs, if the failures occur due to the movement of the sensor nodes or sensor node's energy depletion. This is done by introducing a new method to create alternative paths together with the data routing obtained during the path discovery stage. The goal of this operation is to update and offer new routing information in order to construct the multiple paths resulting in an increased reliability of the sensor network. From the simulation, we have seen that the proposed method shows better results in terms of packet delivery ratio and energy efficiency.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.712-717
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• 2008

In swarm robot systems, each robot must act by itself according to the its states and environments, and if necessary, must cooperate with other robots in order to carry out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, reinforcement learning method with SVM based on structural risk minimization and distributed genetic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. By distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the performance of evolution, selective crossover using the characteristic of reinforcement learning that basis of SVM is adopted in this paper.

• ETRI Journal
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• v.39 no.2
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• pp.151-162
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• 2017

With the remarkable growth in rich media in recent years, people are increasingly exposed to visual information from the environment. Visual information continues to play a vital role in rich media because people's real interests lie in dynamic information. This paper proposes a novel discrete dynamic swarm optimization (DDSO) algorithm for video object tracking using invariant features. The proposed approach is designed to track objects more robustly than other traditional algorithms in terms of illumination changes, background noise, and occlusions. DDSO is integrated with a matching procedure to eliminate inappropriate feature points geographically. The proposed novel fitness function can aid in excluding the influence of some noisy mismatched feature points. The test results showed that our approach can overcome changes in illumination, background noise, and occlusions more effectively than other traditional methods, including color-tracking and invariant feature-tracking methods.