• BMB Reports
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• v.46 no.4
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• pp.225-229
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• 2013

Methylglyoxal (MG) is an endogenous metabolite which is present in increased concentrations in diabetics and reacts with amino acids to form advanced glycation end products. In this study, we investigated whether ferritin enhances DNA cleavage by the reaction of MG with lysine. When plasmid DNA was incubated with MG and lysine in the presence of ferritin, DNA strand breakage was increased in a dose-dependent manner. The ferritin/MG/lysine system-mediated DNA cleavage was significantly inhibited by reactive oxygen species (ROS) scavengers. These results indicated that ROS might participate in the ferritin/MG/lysine system-mediated DNA cleavage. Incubation of ferritin with MG and lysine resulted in a time-dependent release of iron ions from the protein molecules. Our data suggest that DNA cleavage caused by the ferritin/MG/lysine system via the generation of ROS by the Fenton-like reaction of free iron ions released from oxidatively damaged ferritin.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.822-827
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• 2004

In this paper, an intelligent fault diagnosis system is proposed for induction motors through the combination of feature extraction, genetic algorithm (GA) and neural network (ANN) techniques. Features are extracted from motor vibration signals, while reducing data transfers and making on-line application available. GA is used to select most significant features from whole feature database and optimize the ANN structure parameter. Optimized ANN diagnoses the condition of induction motors online after trained by the selected features. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origin on the induction motors. The results of the test indicate that the proposed system is promising for real time application.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.8
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• pp.623-631
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• 2003

We have fabricated the two-dimensional inverted pendulum system and designed its controller. The two-dimensional inverted pendulum system, which is composed of X-Y table, is actuated through timing belt by each of two geared DC motors. And the control goal is that the rod is always kept to a vertical position to any distrubance and is quickly moved to the desired position. Because this system has generally nonlinear dynamic characteristics and X-axis and Y-axis move together, it is very difficult to find its exact mathematical model and to design its controller. Therefore, we have designed the PID controller with simple structure and excellent performance. Genetic algorithm(GA), which is blown as one of probabilistic searching methods, and human's heuristic control strategy are introduced to design an optimal PID controller. The usefulness of the proposed GA based PID coefficient searching technique is verified through the experiments and computer simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.2
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• pp.107-113
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• 2010

In this paper, a prototype of cooperative work model for multi-robots system is introduced and the evolutionary approach is applied to generate the motions for the cooperative works of multi-robots system using genetic algorithm. The cooperative tasks can be performed by a humanoid robot and a mobile robot to deliver objects from shelves. Generation of the humanoid motions such as pick up, rotation, and place operation for the cooperative works are evolved. Modeling and computer simulation for the cooperative robots system are executed in Webots environments. Experimental results show the feasible and reasonable solutions for humanoid cooperative tasks are obtained.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.403-410
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• 1998

An immune system has powerful abilities such as memory recognition and learning how to respond to invading antigens, and has been applied to many engineering algorithms in recent year. In this paper, the combined optimization algorithm (Immune-Genetic Algorithm: IGA) is proposed for multi-optimization problems by introduction the capability of the immune system that controls the proliferation of clones to the genetic algorithm. The new combined algorithm is applied to minimize the total weight of the rotor shaft and the transmitted forces at the bearings in order to demonstrate the merit of the combined algorithm. The inner diameter of the shaft and the bearing stiffness are chosen as the design variables. the results show that the combined algorithm can reduce both the weight of the shaft and the transmitted forces at the bearing with dynamic constraints.

• Journal of Information Technology Applications and Management
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• v.12 no.3
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• pp.41-56
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• 2005

Product recommender system is one of the most popular techniques for customer relationship management. In addition, collaborative filtering (CF) has been known to be one of the most successful recommendation techniques in product recommender systems. However, CF has some limitations such as sparsity and scalability problems. This study proposes hybrid cluster analysis and case-based reasoning (CBR) to address these problems. CBR may relieve the sparsity problem because it recommends products using customer profile and transaction data, but it may still give rise to scalability problem. Thus, this study uses cluster analysis to reduce search space prior to CBR for scalability Problem. For cluster analysis, this study employs hybrid genetic and K-Means algorithms to avoid possibility of convergence in local minima of typical cluster analyses. This study also develops a Web-based prototype system to test the superiority of the proposed model.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.307-314
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• 2018

In this paper, analytical modeling of lift and drag forces in permanent magnet electrodynamic suspension systems (PM EDSs) are presented. After studying the impacts of PM dimensions on the permanent magnetic field and developed lift force, it is indicated that there is an optimum PM length in a specified thickness for a maximum lift force. Therefore, the optimum PM length for achieving maximum lift force is obtained. Afterward, an objective design optimization is proposed to increase the lift force and to decrease the material cost of the system by using Genetic Algorithm. The results confirm that the required values of the lift force can be achieved; while, reducing the system material cost. Finite Element Analysis (FEA) and experimental tests are carried out to evaluate the effectiveness of the PM EDS system model and the proposed optimization method. Finally, a number of design guidelines are extracted.

• Journal of Intelligence and Information Systems
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• v.3 no.2
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• pp.83-91
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• 1997

DELVAUX is an inductive learning environment that learns Bayesian classification rules from a set o examples. In DELVAUX, a genetic a, pp.oach is employed to learn the best rule-set, in which a population consists of rule-sets and rule-sets generate offspring by exchanging some of their rules. We have explored a meta-learning a, pp.oach in the DELVAUX learning environment to improve the classification performance of the DELVAUX system. The meta-learning a, pp.oach learns the bias of a classifier so that it can evaluate the prediction made by the classifier for a given example and thereby improve the overall performance of a classifier system. The paper discusses the meta-learning a, pp.oach in details and presents some empirical results that show the improvement we can achieve with the meta-learning a, pp.oach.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.383-386
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• 2005

This study proposes a case based reasoning system with two dimensional reduction techniques. In this study, vertical and horizontal dimensions of the research data are reduced through hybrid feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed technique may improve the classification accuracy and outperform various optimized models of typical CBR system.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.68-70
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• 1996

A fuzzy network using genetic algorithm is investigated in the context of control for finite dimensional nonlinear discrete systems. The proposed FN(Fuzzy Network) constructed to identify various parameter of fuzzy control is used for the nonlinear system control. Each of two FN, presented FN control system is based on a framework of closed loop control. A proposed FNN model trains using the modeling error and the closed loop error. That case study shows that the presented FN model and closed loop control system is very useful in practical sense.