• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.574-579
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• 2009

There have been much difficulties to construct an optimized neural network in complex nonlinear regression problems such as selecting the networks structure and avoiding overtraining problem generated by noise. In this paper, we propose a stepwise constructive method for neural networks using a flexible incremental algorithm. When the hidden nodes are added, the flexible incremental algorithm adaptively controls the number of hidden nodes by a validation dataset for minimizing the prediction residual error. Here, the ELM (Extreme Learning Machine) was used for fast training. The proposed neural network can be an universal approximator without user intervene in the training process, but also it has faster training and smaller number of hidden nodes. From the experimental results with various benchmark datasets, the proposed method shows better performance for real-world regression problems than previous methods.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.3
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• pp.25-34
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• 1992

This paper proposes a Self-Tuning control algorithm for tracking the reference trajectory by measuring the end-point of robot manipulator whose links are light and flexible, and the performance of it is tested through the computer simulation. As an object of system, a flexible robot manipulator with two-links is considered and an assumed mode shape method including gravity force is adopted to analyze the vibration modes for each links and dynamics equation is derived. The controller is designed as a combined form which consists of dynamic feedforward compensator and self-tuning feedback controller. The one supplies nominal torque and the other supplies variational torque to manipulator. Apart from the, K-incremental predictor is also proposed in order to eliminate the offset error. and it shows that the result of simulation adapted well to load change and rapid velocity.

• Structural Engineering and Mechanics
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• v.7 no.4
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• pp.345-360
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• 1999

A general framework for the nonlinear geometric analysis of elastic space trusses is presented. Both total Lagrangian and finite incremental formulations are derived from the three key ingredients of statics, kinematics and constitutive law. Particular features of the general methodology include the preservation of static-kinematic duality through the concept of fictitious forces and deformations, and an exact description for arbitrarily large displacements, albeit small strain, that can be specialized to any order of geometrical nonlinearity. As for the numerical algorithm, we consider specifically the finite incremental case and suggest the use of a conventional, simple and flexible arc-length based method. Numerical examples are presented to illustrate and validate the accuracy of the approach.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.163-167
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• 2003

A novel method for 3-dimensional dynamic analysis of marine slender structure gas been developed by using Euler parameters. The Euler parameter rotation, which is being widely used in aerospace vehicle dynamics and multi-body dynamics, has been applied to elastic structure analysis. Large deformation of flexible slender structures is described by means of Euler parameters. Euler parameter method is implemented effectively in incremental-iterative algorithm for 3D dynamic analysis. The normalization constraint of Euler parameters is efficiently satisfied by means of a sequential updating method.

• Journal of KIISE:Databases
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• v.31 no.6
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• pp.699-709
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• 2004

XML is increasingly important in data exchange and information management. Starting point for retrieving the structure and integrating the documents efficiently is clustering the documents that have similar structure. The reason is that we can retrieve the documents more flexible and faster than the method treating the whole documents that have different structure. Therefore, in this paper, we propose the similar structure-based incremental clustering method useful for retrieving the structure of XML documents and integrating them. As a novel method, we use a clustering algorithm for transactional data that facilitates the large number of data, which is quite different from the existing methods that measure the similarity between documents, using vector. We first extract the representative structures of XML documents using sequential pattern algorithm, and then we perform the similar structure based document clustering, assuming that the document as a transaction, the representative structure of the document as the items of the transaction. In addition, we define the cluster cohesion and inter-cluster similarity, and analyze the efficiency of the Proposed method through comparing with the existing method by experiments.