• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.2
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• pp.13-22
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• 2005

To improve the anomaly IDS using system calls, this study focuses on Neural Networks Learning using the Soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern. That is, by changing variable length sequential system call data into a fixed length behavior pattern using the Soundex algorithm, this study conducted neural networks learning by using a backpropagation algorithm with fuzzy membership function. The back-propagation neural networks and Neuro-Fuzzy technique are applied for anomaly intrusion detection of system calls using Sendmail Data of UNM to demonstrate its aspect of he complexity of time, space and MDL performance.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.13-17
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• 2024

In this paper, union-based rule antecedent neuro-fuzzy controller, which can guarantee a parsimonious knowledge base with reduced number of rules, is proposed. The proposed neuro-fuzzy controller allows union operation of input fuzzy sets in the antecedents to cover bigger input domain compared with the complete structure rule which consists of AND combination of all input variables in its premise. To construct the proposed neuro-fuzzy controller, we consider the multiple-term unified logic processor (MULP) which consists of OR and AND fuzzy neurons. The fuzzy neurons exhibit learning abilities as they come with a collection of adjustable connection weights. In the development stage, the genetic algorithm (GA) constructs a Boolean skeleton of the proposed neuro-fuzzy controller, while the stochastic reinforcement learning refines the binary connections of the GA-optimized controller for further improvement of the performance index. An inverted pendulum system is considered to verify the effectiveness of the proposed method by simulation and experiment.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.513-515
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• 1999

In this paper, Neuro-Fuzzy controller which has the characteristic of Fuzzy control and artificial Neural Network is designed A fuzzy rule to be applied is selected automatically by the allocated neurons. The neurons correspond to Fuzzy rules which are created by the expert. In order to adaptivity, the more precise modeling is implemented by error back propagation learning of adjusting the link-weight of fuzzy membership function in Neuro-fuzzy controller. The more classified fuzzy rule is used to include the property of Dual mode Method. To test the effectiveness of the algorithm designed above the experiment for DC servo motor with variable load as variable load plant is implementation.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2018-2020
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• 2001

In this study, a Neuro-Fuzzy Controller which has the characteristic of Fuzzy control and Artificial Neural Network is designed. A fuzzy rule to be applied is automatically selected by the allocated neurons. The neurons correspond to Fuzzy rules are created by an expert. To adapt the more precise modeling is implemented by error back propagation learning of adjusting the link-weight of fuzzy membership function in the Neuro-Fuzzy controller. The more classified fuzzy rule is used to include the property of dual mode method. In order to verify the effectiveness of an algorithm designed above, an operating characteristic of a AC servo motor is investigated.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.4
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• pp.132-139
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• 2002

In GMAW(Gas Metal Arc Welding) processes, bead geometry (penetration, bead width and height) is a criterion to estimate welding quality. Bead geometry is affected by welding current, arc voltage and travel speed, shielding gas, CTWD (contact-tip to workpiece distance) and so on. In this paper, welding process variables were selected as welding current, arc voltage and travel speed. And bead geometry was reasoned from the chosen welding process variables using neuro-fuzzy algorithm. Neural networks was applied to design FL(fuzzy logic). The parameters of input membership functions and those of consequence functions in FL were tuned through the method of learning by backpropagation algorithm. Bead geometry could be reasoned from welding current, arc voltage, travel speed on FL using the results learned by neural networks. On the developed inference system of bead geometry using neuro-furzy algorithm, the inference error percent of bead width was within $\pm$4%, that of bead height was within $\pm$3%, and that of penetration was within $\pm$8%. Neural networks came into effect to find the parameters of input membership functions and those of consequence in FL. Therefore the inference system of welding quality expects to be developed through proposed algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.141-151
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• 1999

The resistance Spot Welding is widely used in the field of assembling the plates. However we don't still have any satisfactory solution, which is non-destructive quality evaluation in real-time or on-line, against it. Moreover, even though the rate of welding under the condition of expulsion has been high until now, quality control of welding against expulsion hasn't still been established. In this paper, it was proposed on the quality assurance technique of resistance spot welding pieces using Neuro-Fuzzy algorithm. Four parameters from electrode separation signal in the case of non-expulsion, and dynamic resistance patterns in the case of expulsion are selected as fuzzy input parameters. The parameters consist of Fuzzy Inference System are determined through Neuro-Learning algorithm. And then, fuzzy Inference System is constructed. It was confirmed that the fuzzy inference values of strength have within ${\pm}$4% error specimen in comparison with real strength for the total strength range, and the specimen percent having within ${\pm}$1% error was 88.8%. According to KS(Korean Industrial Standard), tensile-shear strength limit for electric coated of zinc is 400kgf/mm2. Judging to the quality of welding is good or bad, according to this criterion and the results of inference, the probability of misjudgement that good quality is valuated into poor one was 0.43%, on contrary it was 2.59%. Finally, the proposed Neuro-Fuzzy Inference System can infer the tensile-shear strength of resistance spot welding pieces with high performance for all cases-non-expulsion and expulsion. And On-Line Welding Quality Inspection System will be realized sooner or later.

• Structural Engineering and Mechanics
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• v.45 no.6
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• pp.779-802
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• 2013

In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed that both ANFIS and ANN present good predictions. However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. This paper also highlights the concept of ANNs and ANFIS followed by the detail presentation of the experimental modal analysis for natural frequencies extraction.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1952-1954
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• 2001

In this paper, we propose the neuro-fuzzy modeling method using VmGA (Virus messy Genetic Algorithm) for the complex nonlinear system. VmGA has more effective and adaptive structure than sGA. in this paper, we suggest a new coding method for applying the model's input and output data to the optimal number of rules in fuzzy models and the structure and parameter identification of membership functions simultaneously. The proposed method realizes the optimal fuzzy inference system using the learning ability of neural network. For fine-tune of parameters identified by VmGA, back- propagation algorithm is used for optimizing the parameter of fuzzy set. The proposed fuzzy modeling method is applied to a nonlinear system to prove the superiority of the proposed approach through comparing with ANFIS.

• Journal of Mechanical Science and Technology
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• v.16 no.4
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• pp.454-467
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• 2002

It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. Toidentifythelocation and depth of a crack in a structure, a method is presented in this paper which uses neuro-fuzzy-evolutionary technique, that is, Adaptive-Network-based Fuzzy Inference System (ANFIS) solved via hybrid learning algorithm (the back-propagation gradient descent and the least-squares method) and Continuous Evolutionary Algorithms (CEAs) solving sir ale objective optimization problems with a continuous function and continuous search space efficiently are unified. With this ANFIS and CEAs, it is possible to formulate the inverse problem. ANFIS is used to obtain the input(the location and depth of a crack) - output(the structural Eigenfrequencies) relation of the structural system. CEAs are used to identify the crack location and depth by minimizing the difference from the measured frequencies. We have tried this new idea on beam structures and the results are promising.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.712-715
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• 1997

In image jacobian based visual servoing, generally, inverse jacobian should be calculated by complicated coordinate transformations. These are required excessive computation and the singularity of the image jacobian should be considered. This paper presents a visual servoing to control the pose of the robotic manipulator for tracking and grasping 3-D moving object whose pose and motion parameters are unknown. Because the object is in motion tracking and grasping must be done on-line and the controller must have continuous learning ability. In order to estimate parameters of a moving object we use the kalman filter. And for tracking and grasping a moving object we use a fuzzy inference based reinforcement learning algorithm of dynamic recurrent neural networks. Computer simulation results are presented to demonstrate the performance of this visual servoing