• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1111-1130
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• 2016

Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle Swarm Ant Colony Optimization) based on PSACO (Particle Swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.6
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• pp.504-510
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• 2016

Robots are widely utilized in many fields, and various demands need customized robots. This study proposes an optimal design method based on swarm intelligence for selecting the kinematic parameter of a manipulator according to the task space trajectory desired by the user. The optimal design method is dealt with herein as an optimization problem. This study is based on swarm intelligence-based optimization algorithms (i.e., ant colony optimization (ACO) and particle swarm optimization algorithms) to determine the optimal kinematic parameters of the manipulator. The former is used to select the optimal kinematic parameter values, whereas the latter is utilized to solve the inverse kinematic problem when the ACO determines the parameter values. This study solves a design problem with the PUMA 560 when the desired task space trajectory is given and discusses its results in the simulation part to verify the performance of the proposed design.

• Computers and Concrete
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• v.27 no.5
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• pp.489-512
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• 2021

In this study, two powerful techniques, namely particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were selected and combined with a pre-developed ANN model aiming at improving its performance prediction of the compressive strength of concrete modified with fly ash. To achieve this study's aims, a comprehensive database with 379 data samples was collected from the available literature. The output of the database is the compressive strength (CS) of concrete samples, which are influenced by 9 parameters as model inputs, namely those related to mix composition. The modeling steps related to ICA-ANN (or neuro-imperialism) and PSO-ANN (or neuro-swarm) were conducted through the use of several parametric studies to design the most influential parameters on these hybrid models. A comparison of the CS values predicted by hybrid intelligence techniques with the experimental CS values confirmed that the neuro-swarm model could provide a higher degree of accuracy than another proposed hybrid model (i.e., neuro-imperialism). The train and test correlation coefficient values of (0.9042 and 0.9137) and (0.8383 and 0.8777) for neuro-swarm and neuro-imperialism models, respectively revealed that although both techniques are capable enough in prediction tasks, the developed neuro-swarm model can be considered as a better alternative technique in mapping the concrete strength behavior.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.12
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• pp.981-988
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• 2021

This paper presents a decentralized guidance algorithm for swarm flight of fixed-wing UAVs (Unmanned Aerial Vehicles). Considering swarm flight missions, we assume four representative swarm tasks: gathering, loitering, waypoint/path following, and individual task. Those tasks require several distinct maneuvers such as path following, flocking, and collision avoidance. In order to deal with the required maneuvers, this paper proposes an integrated guidance algorithm based on vector field, augmented Cucker-Smale model, and potential field methods. Integrated guidance command is synthesized with heuristic weights designed for each guidance method. The proposed algorithm is verified through flight tests using up to 19 small fixed-wing UAVs.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.319-326
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• 2009

PSO(Particle Swarm Optimization) is an optimization algorithm in which simple particles search an optimal solution using shared information acquired through their own experiences. PSO applications are so numerous and diverse. Lots of researches have been made mainly on the parameter settings, topology, particle's movement in order to achieve fast convergence to proper regions of search space for optimization. In standard PSO, since each particle uses only information of its and best neighbor, swarm does not explore diverse regions and intended to premature to local optima. In this paper, we propose a new particle's movement strategy in order to explore diverse regions of search space. The strategy is that each particle moves according to relative weights of several better neighbors. The strategy of exploring diverse regions is effective and produces less local optimizations and accelerating of the optimization speed and higher success rates than standard PSO. Also, in order to raise success rates, we propose a strategy for checking whether swarm falls into local optimum. The new PSO algorithm with these two strategies shows the improvement in the search speed and success rate in the test of benchmark functions.

• Journal of Electrical Engineering and Technology
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• v.6 no.6
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• pp.806-816
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• 2011

Interest about social security has recently increased in favor of safety for infrastructure. In addition, advances in computer vision and pattern recognition research are leading to video-based surveillance systems with improved scene analysis capabilities. However, such video surveillance systems, which are controlled by human operators, cannot actively cope with dynamic and anomalous events, such as having an invader in the corporate, commercial, or public sectors. For this reason, intelligent surveillance systems are increasingly needed to provide active social security services. In this study, we propose a core technique for intelligent surveillance system that is based on swarm robot technology. We present techniques for invader enclosing using swarm robots based on multiple distributed object environment. The proposed methods are composed of three main stages: location estimation of the object, specified object tracking, and decision of the cooperative behavior of the swarm robots. By using particle filter, object tracking and location estimation procedures are performed and a specified enclosing point for the swarm robots is located on the interactive positions in their coordinate system. Furthermore, the cooperative behaviors of the swarm robots are determined via the result of path navigation based on the combination of potential field and wall-following methods. The results of each stage are combined into the swarm robot-based invader-enclosing technique on multiple distributed object environments. Finally, several simulation results are provided to further discuss and verify the accuracy and effectiveness of the proposed techniques.

• Journal of Digital Convergence
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• v.15 no.8
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• pp.195-202
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• 2017

Unmanned Aerial Vehicles (UAV) have mostly been used for military purposes but with the progress in ICT and reduced manufacturing costs, they are increasingly used for various private services. UAVs are expected to carry out autonomous flying in the future. In order to carry out complex tasks, swarm flights are essential. Although the swarm flights has been researched a lot due to its different network and infrastructure from the existing UAV system, There are still not enough study on security threats and requirements for the secure swarm flights. In this paper, to solve these problems, UAV autonomous flight technology is defined based on US Army Corps of Engineers (USACE) and Air Force Research Laboratory (AFRL), and swarm flights and security threat about it are classified. And then we defined and compared security requirements according to security threats of each swarm flights so as to contribute to the development of secure UAC swarm flights in the future.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.87-92
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• 2016

In this paper, optimization technique such as particle swarm optimization was used to optimize the parameters of fuzzy Extreme Learning Machine. While the learning speed of conventional neural networks is very slow, that of Extreme Learning Machine is very fast. Fuzzy Extreme Learning Machine is composed of the Extreme Learning Machine with very fast learning speed and fuzzy logic which can represent the linguistic information of the field experts. The general sigmoid function is used for the activation function of Extreme Learning Machine. However, the activation function of Fuzzy Extreme Learning Machine is the membership function which is defined in the procedure of fuzzy C-Means clustering algorithm. We optimize the parameters of the membership functions by using optimization technique such as Particle Swarm Optimization. In order to validate the classification capability of the proposed classifier, we make several experiments with the various machine learning datas.

• Convergence Security Journal
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• v.23 no.3
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• pp.65-71
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• 2023

The importance of combat drones has been highlighted through the recent outbreak of the Russia-Ukraine war. The combat drones play a significant role as a a game changer that alters the conventional wisdom of traditional warfare. Many pundits expect the role of combat swarm drones would be more crucial in the future warfare. In this regard, this paper aims to analyze the development of artificial intelligence-enabled combat swarm drones. To transform the human-operated swarm drones into fully autonomous weaponry system our suggestions are as follows. Developments of (1) AI algorithms for optimized swarm drone operations, (2) decentralized command and control system, (3) inter-drones' mission analysis and allocation technology, (4) enhanced drone communication security and (5) set up of ethical guideline for the autonomous system. Specifically, we suggest the development of AI algorithms for drone collision avoidance and moving target attacks. Also, in order to adjust rapidly changing military environment, decentralized command and control system and mission analysis allocation technology are necessary. Lastly, cutting-edging secure communication technology and concrete ethical guidelines are essential for future AI-enabled combat swarm drones.

• The Journal of the Petrological Society of Korea
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• v.27 no.2
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• pp.73-84
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• 2018

In the northern Cheongsong, there are occurred igneous intrusions: Cheongsong dike swarm, Jungtaesan laccolith, Galpyeongji stock. The swarm is composed of rhyolitic dikes that have developed many various spherulites. The dikes represent an geometrically radical pattern centering the Galpyeongji stock, but also geochemistry of the intrusions indicate the swarm source. Here we report the compositional data for 28 samples from the three intrusions. All of the intrusions belong to rhyolitic composition, but according to compositional correlation, there are considerable overlaps between intrusion compositions. In particular, the Cheongsong dike swarm is divided into several dike groups by rock color and shows compositional diversity, but the composition of the dikes generally overlap with compositions of other intrusions. The Jungtaesan laccolith is enriched in alkali, $K_2O$ and $Al_2O_3$ and depleted in $Fe_2O_3{^t}$, $TiO_2$ and REE compared to the Cheongsong dike swarm. In contrast, the Galpyeongji stock is narrow in composition range, and commonly has sharp compositional overlaps with the Cheongsong dike swarm. According to the compositional correlations, the stock is considered to be a source of the swarm and it is connected to an episode of volcanism.