• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.7
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• pp.524-531
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• 2013

This work aims at developing a RTB (Rotor Track and Balance) system to alleviate imbalances originating from various sources encountered during blade manufacturing process and environmental factors. The analytical RTB model is determined based on the linear regression analysis to relate the RTB adjustment parameters and their track and vibration results. The model is validated using the flight test data of a full helicopter. It is demonstrated that the linearized model has been correlated well with the test data. A hybrid optimization problem is formulated to find the best solution of the RTB adjustment parameters using the genetic algorithm combined with the PSO (Particle Swarm Optimization) algorithm. The optimization results reveal that both track deviations and vibration levels under various flight conditions become decreased within the allowable tolerances.

• Journal of KIISE
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• v.43 no.2
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• pp.204-211
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• 2016

In this paper, we propose an automatic melody composition system that can generate a sophisticated melody by adding non-harmony tone in the given chord progression. An overall procedure consists of two steps, which are the rhythm generation and melody generation parts. In the rhythm generation part, we designed new fitness functions for rhythm that can be controlled by a user setting parameters. In the melody generation part, we designed new fitness functions for melody based on harmony theory. We also designed evolutionary operators that are conducted by considering a musical context to improve computational efficiency. In the experiments, we compared four metaheuristics to optimize the rhythm fitness functions: Simple Genetic Algorithm (SGA), Elitism Genetic Algorithm (EGA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Furthermore, we compared proposed genetic algorithm for melody with the four algorithms for verifying performance. In addition, composition results are introduced and analyzed with respect to musical correctness.

• Journal of the Korea Society for Simulation
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• v.24 no.2
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• pp.1-9
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• 2015

The tactical employment is a critical factor to win the war in the modern battlefield. To apply optimized tactics, it needs analyses related to a battle system. Normally, M&S (Modeling & Simulation) has been studied to analyze data in general problems. However, this method is not suitable for military simulations because there are many variables which make complex interaction in the system. For this reason, we suggested the optimized simulation framework based on the M&S by using DPSO (Discrete binary version of PSO) algorithm. This optimized simulation framework makes the best tactical employment to reduce the searching time compared with the normal M&S used by Monte Carlo search method. This paper shows an example to find the best combination of anti-torpedo scenario in a short searching time. From the simulation example, the optimized simulation framework presents the effectiveness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.943-948
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• 2022

Drones have been widely used in various fields due to the rapid development of done-related technologies, which causes various problems. The schemes which can track target drones by using signal transmitted by target drones have been investigated as a key technology for anti-drone systems to solve these problems. In this paper, we investigate an illegal drone tracking system based on swarming flight that consists of multiple small drones in order to resolve the limitations of a conventional system that consists of a single drone. In addition, we also propose a scheme with which we can adaptively adjust the separation distance between small drones in a swarm according to channel situations. We analyzed the performance of the proposed scheme in terms of success ratio and the number of movements. The proposed scheme can improve the success ratio and the number of movements by 170% and 63% respectively, compared to the conventional scheme.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.109-116
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• 2023

In this paper, we study the containment control problem for second-order multi-agent systems, which consists of multiple leaders and followers. The goal is to drive the followers toward the convex hull spanned by the leaders. Thus, the swarm behavior can be obtained by controlling the entire group by the leaders. This paper considers the leaders move at a constant speed and the followers have input saturations. Moreover, we assume that the followers can exchange information with neighbors, and only relative state information is available. Under these assumptions, we propose the Proportional-Integral based distributed control algorithm to solve the containment control problem with moving leaders. Moreover, based on Lasalle's invariance principle, the conditions for the control gains that guarantee the convergence of the followers to the convex hull spanned by the leaders are investigated, and it was shown that it can be designed only using the system parameter. Finally, the simulations are conducted to validate the theoretical result.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.526-533
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• 2014

In this study, we introduce design methodology to develop a guidance for issuing heavy rainfall warning by using both RBFNNs(Radial basis function neural networks) and SVR(Support vector regression) model, and then carry out the comparative studies between two pattern classifiers. Individual classifiers are designed as architecture realized with the aid of optimization and pre-processing algorithm. Because the predictive performance of the existing heavy rainfall forecast system is commonly affected from diverse processing techniques of meteorological data, under-sampling method as the pre-processing method of input data is used, and also data discretization and feature extraction method for SVR and FCM clustering and PSO method for RBFNNs are exploited respectively. The observed data, AWS(Automatic weather wtation), supplied from KMA(korea meteorological administration), is used for training and testing of the proposed classifiers. The proposed classifiers offer the related information to issue a heavy rain warning in advance before 1 to 3 hours by using the selected meteorological data and the cumulated precipitation amount accumulated for 1 to 12 hours from AWS data. For performance evaluation of each classifier, ETS(Equitable Threat Score) method is used as standard verification method for predictive ability. Through the comparative studies of two classifiers, neuro-fuzzy method is effectively used for improved performance and to show stable predictive result of guidance to issue heavy rainfall warning.

• Journal of Power Electronics
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• v.17 no.2
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• pp.476-489
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• 2017

Under partial shading conditions (PSCs), multiple maximums may be exhibited on the P-U curve of string inverter photovoltaic (PV) systems. Under such conditions, heuristic methods are invalid for extracting a global maximum power point (GMPP); intelligent algorithms are time-consuming; and model-based methods are complex and costly. To overcome these shortcomings, a novel hybrid MPPT (MPF-IP&O) based on a model-based peak forecasting (MPF) method and an improved perturbation and observation (IP&O) method is proposed. The MPF considers the influence of temperature and does not require solar radiation measurements. In addition, it can forecast all of the peak values of the PV string without complex computation under PSCs, and it can determine the candidate GMPP after a comparison. Hence, the MPF narrows the searching range tremendously and accelerates the convergence to the GMPP. Additionally, the IP&O with a successive approximation strategy searches for the real GMPP in the neighborhood of the candidate one, which can significantly enhance the tracking efficiency. Finally, simulation and experiment results show that the proposed method has a higher tracking speed and accuracy than the perturbation and observation (P&O) and particle swarm optimization (PSO) methods under PSCs.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.9
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• pp.1392-1401
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• 2017

In this study, the design methodology of pattern classification is introduced for avoiding faults through partial discharge occurring in the power facilities and local sites. In order to classify some partial discharge types according to the characteristics of each feature, the model is constructed by using the Radial Basis Function Neural Networks(RBFNNs) and Particle Swarm Optimization(PSO). In the input layer of the RBFNNs, the feature vector is searched and the dimension is reduced through Principal Component Analysis(PCA) and PSO. In the hidden layer, the fuzzy coefficients of the fuzzy clustering method(FCM) are tuned using PSO. Raw datasets for partial discharge are obtained through the Motor Insulation Monitoring System(MIMS) instrument using an Epoxy Mica Coupling(EMC) sensor. The preprocessed datasets for partial discharge are acquired through the Phase Resolved Partial Discharge Analysis(PRPDA) preprocessing algorithm to obtain partial discharge types such as void, corona, surface, and slot discharges. Also, when the amplitude size is considered as two types of both the maximum value and the average value in the process for extracting the preprocessed datasets, two different kinds of feature datasets are produced. In this study, the classification ratio between the proposed RBFNNs model and other classifiers is shown by using the two different kinds of feature datasets, and also we demonstrate the proposed model shows superiority from the viewpoint of classification performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.922-934
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• 2015

In this paper, The classification between precipitation echo(PRE) and non-precipitation echo(N-PRE) (including ground echo and clear echo) is carried out from weather radar data using neuro-fuzzy algorithm. In order to classify between PRE and N-PRE, Input variables are built up through characteristic analysis of radar data. First, the event classifier as the first classification step is designed to classify precipitation event and non-precipitation event using input variables of RBFNNs such as DZ, DZ of Frequency(DZ_FR), SDZ, SDZ of Frequency(SDZ_FR), VGZ, VGZ of Frequency(VGZ_FR). After the event classification, in the precipitation event including non-precipitation echo, the non-precipitation echo is completely removed by the echo classifier of the second classifier step that is built as Type-2 FCM based RBFNNs. Also, parameters of classification system are acquired for effective performance using PSO(Particle Swarm Optimization). The performance results of the proposed echo classifier are compared with CZ. In the sequel, the proposed model architectures which use event classifier as well as the echo classifier of Interval Type-2 FCM based RBFNN show the superiority of output performance when compared with the conventional echo classifier based on RBFNN.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.88-102
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• 2016

This paper considers the allocation and engagement scheduling problem of interceptor missiles, and the problem was formulated by using MIP (mixed integer programming) in the previous research. The objective of the model is the maximization of total intercept altitude instead of the more conventional objective such as the minimization of surviving target value. The concept of the time window was used to model the engagement situation and a continuous time is assumed for flying times of the both missiles. The MIP formulation of the problem is very complex due to the complexity of the real problem itself. Hence, the finding of an efficient optimal solution procedure seems to be difficult. In this paper, an efficient genetic algorithm is developed by improving a general genetic algorithm. The improvement is achieved by carefully analyzing the structure of the formulation. Specifically, the new algorithm includes an enhanced repair process and a crossover operation which utilizes the idea of the PSO (particle swarm optimization). Then, the algorithm is throughly tested on 50 randomly generated engagement scenarios, and its performance is compared with that of a commercial package and a more general genetic algorithm, respectively. The results indicate that the new algorithm consistently performs better than a general genetic algorithm. Also, the new algorithm generates much better results than those by the commercial package on several test cases when the execution time of the commercial package is limited to 8,000 seconds, which is about two hours and 13 minutes. Moreover, it obtains a solution within 0.13~33.34 seconds depending on the size of scenarios.