• Journal of Korean Society on Water Environment
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• v.23 no.5
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• pp.697-704
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• 2007

This study proposes an integrated technique of Genetic Algorishim (GA) and Geographic Information System (GIS) for designing the water quality monitoring networks. To develop solution scheme of the integrated system, fitness functions are defined by the linear combination of five criteria which stand for the operation objectives of water quality monitoring stations. The criteria include representativeness of a river system, compliance with water quality standards, supervision of water use, surveillance of pollution sources and examination of water quality changes. The fitness level is obtained through calculations of the fitness functions and input data from GIS. To find the most appropriate parameters for the problems, the sensitivity analysis is performed for four parameters such as number of generations, population sizes, probability of crossover, and probability of mutation. Using the parameters resulted from the sensitivity analysis, the developed system proposed 110 water quality monitoring stations in the Nakdong River. This study demonstrates that the integrated technique of GA and GIS can be utilized as a decision supporting tool in optimized design for a water quality monitoring network.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.40 no.5
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• pp.317-325
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• 2003

The vibration, which often occurred in a two inertia motor system, makes it difficult to achieve a quick response of speed and disturbance rejection. This paper provides an autonomous pole assignment technique for three kinds of speed controllers (I-P, I-PD, and State feedback) using GAs(Genetic Algorithms) for a two-inertia motor system. Firstly, the optimal parameters are chosen using GAs in view of reducing overshoot and settling time, then those are used in computing the gains of each controller. Some simulation results verify the effectiveness of the proposed design. The proposed controller is expected to be the autonomous design way for controlling a two-inertia motor system with flexible shaft.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.8
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• pp.471-480
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• 2003

The design of a measurement system to perform Harmonic State Estimation (HSE) is a very complex problem. Among the reasons for its complexity are the system size, conflicting requirements of estimator accuracy, reliability in the presence of transducer noise and data communication failures, adaptability to change in the network topology and cost minimization. In particular, the number of harmonic instruments available is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents a new HSE algorithm which is based on an optimal placement of measurement points using Genetic Algorithms (GAs) which is widely used in areas such as: optimization of the objective function, learning of neural networks, tuning of fuzzy membership functions, machine learning, system identification and control. This HSE has been applied to the Simulation Test Power System for the validation of the new HSE algorithm. The study results have indicated an economical and effective method for optimal placement of measurement points using Genetic Algorithms (GAs) in the Harmonic State Estimation (HSE).

• Journal of the Korean Society for Precision Engineering
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• v.24 no.6
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• pp.86-95
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• 2007

This paper presents the optimal design of an exhaust system of a vacuum-compatible air bearing using a genetic algorithm. To use the air bearings in vacuum conditions, the differential exhaust method is adopted to minimize the air leakage, which prevents air from leaking into a vacuum chamber by recovering air through several successive seal stages in advance. Therefore, the design of the differential exhaust system is very important because several design parameters such as the number of seals, diameter and length of an exhaust tube, pumping speed and ultimate pressure of a vacuum pump, seal length and gap(bearing clearance) influence on the air leakage, that is, chamber's degree of vacuum. In this paper, we used a genetic algorithm to optimize the design parameters of the exhaust system of a vacuum-compatible air bearing under the several constraint conditions. The results indicate that chamber's degree of vacuum after optimization improved dramatically compared to the initial design, and that the distribution of the spatial design parameters, such as exhaust tube diameter and seal length, was well achieved, and that technical limit of the pumping speed was well determined.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1292-1298
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• 2017

In this paper, we represent a nonlinear identification of electronic brake pedal behavior in the brake-by-wire (BBW) system based on hybrid group method of data handling (GMDH) and genetic algorithm (GA). A GMDH is a kind of multi-layer network with a structure that is determined through training and which can express nonlinear dynamics as a mathematical model. The GA is used in the GMDH, enabling each neuron to search for its optimal set of connections with the preceding layer. The results obtained with this hybrid approach were compared with different nonlinear system identification methods. The experimental results showed that the hybrid approach performs better than the other methods in terms of root mean square error (RMSE) and correlation coefficients. The hybrid GMDH/GA approach was effective for modeling and predicting the brake pedal system under random braking conditions.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.131-140
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• 2023

Network security situational awareness systems helps in better managing the security concerns of a network, by monitoring for any anomalies in the network connections and recommending remedial actions upon detecting an attack. An Intrusion Detection System helps in identifying the security concerns of a network, by monitoring for any anomalies in the network connections. We have proposed a CRF based IDS system using genetic search feature selection algorithm for network security situational awareness to detect any anomalies in the network. The conditional random fields being discriminative models are capable of directly modeling the conditional probabilities rather than joint probabilities there by achieving better classification accuracy. The genetic search feature selection algorithm is capable of identifying the optimal subset among the features based on the best population of features associated with the target class. The proposed system, when trained and tested on the bench mark NSL-KDD dataset exhibited higher accuracy in identifying an attack and also classifying the attack category.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.24-26
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• 2005

At present, KS-1000 which is one of a commercial measurement instrument for motor fault diagnosis has been used in industrial field. The measurement system of KS-1000 is composed of three part : harmonic acquisition, signal processing by KS-1000 algorithm, diagnosis for motor fault. First of all, voltage signal taken from harmonic sensor is analysed for frequency by KS-1000 algorithm. Then, based on the result values of analysis skilled expert makes a judgment about whether motor system is the abnormality or degradation state. But the expert system such a motor fault diagnosis is very difficult to bring the expectable results by mathematical modeling due to the complexity of judgment process. In this reason, we propose an automation system using fuzzy model based on genetic algorithm(GA) that builded a qualitative model of a system without priori knowledge about a system provided numerical input output data.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.67-72
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• 2004

Generally, the commercial truck has the hydraulic cab tilting system which absorb the vibrations and impacts of the cab. The cab tilting system is equipped for the maintenance and inspection of truck. And it is very important to help user's feeling of driving and convenience. But when the truck cab is tilted, existing model has serious problem of vibration. To satisfy customer's requirements for convenience, it is necessary to improve the hydraulic truck cab tilting system. In this study, the optimization of cab tilting system is carried out by using the G.A to reduce the vibration. The results show that the vibration is reduced and the mean velocity of tilting is improved. In conclusion the improved cab tilting system can be designed and the possibility of optimal design for cab tilting system by using the GA is testified.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.3
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• pp.46-54
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• 1997

In this paper, we psropose the feedback linearization technique for a nonlinear system using genetic algorithms (GAs) and fuzzy logic system. The proposed control scheme approximates the nonlinear term of a nonlinear system using the fuzzy logic system and computes the control input for cancelling the nonlinear term. Then in the fuzzy logic system, the number and shape of membership function of the premise aprt will be tuned to minimize the control error boundary using GAs. And the parameters of the consequence of fuzzy rule will be tuned by the adaptive laws based on lyapunov stability theory in order to guarantee the closed loop stability of control system. The evolution of fuzzy logic system is processed during the on-line adaptive control. The effectiveness of proposed method will be demonstrated by computer simulation of simple nonlinear sytem.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.7
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• pp.961-966
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• 2008

Direct and maternal genetic parameters for production traits in 1,642 pigs and maternal genetic correlations among production (1,642 pigs) and feed efficiency (380 boars) traits were estimated in 7 generations of a Duroc population. Traits studied were daily gain (DG), intramuscular fat (IMF), loineye area (LEA), backfat thickness (BF), daily feed intake (FI), feed conversion ratio (FCR) and residual feed intake (RFI). The RFI was calculated as the difference between actual and predicted feed intake. The predicted feed intake was estimated by adjusting the initial test weight, DG and BF. Data for production traits were analyzed using four alternative animal models (including direct, direct+maternal permanent environmental, or direct+maternal genetic+maternal permanent environmental effects). Direct heritability estimates from the model including direct and all maternal effects were $0.41{\pm}0.04$ for DG, $0.27{\pm}0.04$ for IMF, $0.52{\pm}0.06$ for LEA and $0.64{\pm}0.04$ for BF. Estimated maternal heritabilities ranged from $0.04{\pm}0.04$ to $0.15{\pm}0.05$ for production traits. Antagonistic relationships were observed between direct and maternal genetic effects ($r_{am}$) for LEA (-0.21). Maternal genetic correlations of feed efficiency traits with FI ($r_g$ of FI with FCR and RFI were $0.73{\pm}0.06$ and $0.90{\pm}0.05$, respectively) and LEA (rg of LEA with FCR and RFI were $-0.48{\pm}0.05$ to $-0.61{\pm}0.05$, respectively) were favorable. The estimated moderate genetic correlations between direct and maternal genetic effects for IMF and LEA indicated that maternal effects has an important role in these traits, and should be accounted for in the genetic evaluation system.