• Korean Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.77-84
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• 1997

This paper is concerning the development of multiple neural networks system of problem domains where the complete input space can be decomposed into several different regions, and these are known prior to training neural networks. We will adopt oblique decision tree to represent the divided input space and sel ect an appropriate subnetworks, each of which is trained over a different region of input space. The overall architecture of multiple neural networks system, called the federated architecture, consists of a facilitator, normal subnetworks, and tile networks. The role of a facilitator is to choose the subnetwork that is suitable for the given input data using information obtained from decision tree. However, if input data is close enough to the boundaries of regions, there is a large possibility of selecting the invalid subnetwork due to the incorrect prediction of decision tree. When such a situation is encountered, the facilitator selects a tile network that is trained closely to the boundaries of partitioned input space, instead of a normal subnetwork. In this way, it is possible to reduce the large error of neural networks at zones close to borders of regions. The validation of our approach is examined and verified by applying the federated neural networks system to the configuration design of a midship structure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.9
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• pp.738-745
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• 2012

This paper proposes an approximated soft decision demapping algorithm with low computational complexity for coded 4+12+16 amplitude phase shift keying (APSK) in an additive white Gaussian noise (AWGN) channel. To derive the proposed algorithm, we approximate the decision boundaries for 4+12+16 APSK symbols, and then decide the log likelihood ratio (LLR) value for each bit from the approximated decision boundaries. Although the proposed algorithm shows about 0.6~1.1dB degradation on the error performance compared with the conventional max-log algorithm, it gives a significant result in terms of the computational complexity.

• Proceedings of the IEEK Conference
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• 2000.06c
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• pp.177-180
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• 2000

Recently, a feature extraction method based on decision boundary has been proposed for neural networks. The method is based on the fact that all the features necessary to achieve the same classification accuracy as in the original space can be obtained from the vectors normal to decision boundaries. However, the normal vector was estimated numerically. resulting in inaccurate estimation and a long computational time. In this paper. we propose a new method to calculate the normal vector analytically. Experiments show that the proposed method provides a better performance.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.89-102
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• 2013

The paper presents a fuzzy based impulse noise filter for both gray scale and color images. The proposed approach is based on the technique of boundary discriminative noise detection. The algorithm is a multi-step process comprising detection, filtering and color correction stages. The detection procedure classifies the pixels as corrupted and uncorrupted by computing decision boundaries, which are fuzzified to improve the outputs obtained. In the case of color images, a correction term is added by examining the interactions between the color components for further improvement. Quantitative and qualitative analysis, performed on standard gray scale and color image, shows improved performance of the proposed technique over existing state-of-the-art algorithms in terms of Peak Signal to Noise Ratio (PSNR) and color difference metrics. The analysis proves the applicability of the proposed algorithm to random valued impulse noise.

• Journal of Ocean Engineering and Technology
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• v.26 no.4
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• pp.23-29
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• 2012

Korea has maritime jurisdiction over an area 4.5 times larger than the nation's inland area, but negotiations with surrounding nations on the maritime boundary delimitation have still not been completed satisfactorily. In particular, maritime boundary delimitation has become an important issue in terms of maritime security and resource exploration. Considering national interests, the delimitation of the maritime boundary is essential. However, no system to help the decision-makers involved in maritime boundary delimitation has yet been systematically constructed. Therefore, the aim of this study was the development of a system to support such decision-making. In this study, considerations related to maritime boundary delimitation were investigated through expert advice and international precedents. Based on these considerations, data were collected from several organizations, and a spatial database was systematically constructed. Finally, MBDSS (maritime boundary delimitation support system) was developed to support maritime boundary delimitation. This GIS-based system provides visual information about the considerations for the maritime boundary delimitation. Thus, it could help decision-makers to choose appropriate boundaries during the negotiation. Furthermore, this system is expected to be utilized as a scientific tool on the delimitation of maritime boundaries.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.14-18
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• 2002

The perceptron algorithm, which is one of a class of gradient descent techniques, has been widely used in pattern recognition to determine linear decision boundaries. However, it may not give desirable results when pattern sets are nonlinerly separable. A fuzzy version was developed to male up for the weaknesses in the crisp perceptron algorithm. This was achieved by assigning memberships to the pattern sets. However, still another drawback exists in that the pattern memberships do not consider class typicality of the patterns. Therefore, we propose a possibilistic approach to the crisp perceptron algorithm. This algorithm combines the linearly separable property of the crisp version and the convergence property of the fuzzy version. Several examples are given to show the validity of the method.

• Earthquakes and Structures
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• v.27 no.3
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• pp.177-189
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• 2024

Accurately predicting the failure modes of reinforced concrete (RC) columns is essential for structural design and assessment. In this study, the challenges of imbalanced datasets and complex feature selection in machine learning (ML) methods were addressed through an optimized ML approach. By combining feature selection and oversampling techniques, the prediction of seismic failure modes in rectangular RC columns was improved. Two feature selection methods were used to identify six input parameters. To tackle class imbalance, the Borderline-SMOTE1 algorithm was employed, enhancing the learning capabilities of the models for minority classes. Eight ML algorithms were trained and fine-tuned using k-fold shuffle split cross-validation and grid search. The results showed that the artificial neural network model achieved 96.77% accuracy, while k-nearest neighbor, support vector machine, and random forest models each achieved 95.16% accuracy. The balanced dataset led to significant improvements, particularly in predicting the flexure-shear failure mode, with accuracy increasing by 6%, recall by 8%, and F1 scores by 7%. The use of the Borderline-SMOTE1 algorithm significantly improved the recognition of samples at failure mode boundaries, enhancing the classification performance of models like k-nearest neighbor and decision tree, which are highly sensitive to data distribution and decision boundaries. This method effectively addressed class imbalance and selected relevant features without requiring complex simulations like traditional methods, proving applicable for discerning failure modes in various concrete members under seismic action.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.151-152
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• 2012

This paper proposes a Scene Change Detection method using the local decision tree and clustering. The local decision tree detects cluster boundaries wherein local scenes occur, in such a way as to compare time similarity distributions among the difference values between detected scenes and their adjacent frames, and group an unbroken sequence of frames with similarities in difference value into a cluster unit.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.6
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• pp.1199-1203
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• 2012

This paper proposes a Scene Change Detection method using the local decision tree and clustering. The local decision tree detects cluster boundaries wherein local scenes occur, in such a way as to compare time similarity distributions among the difference values between detected scenes and their adjacent frames, and group an unbroken sequence of frames with similarities in difference value into a cluster unit.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.2
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• pp.89-96
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• 1998

Conventional block-based video coders induce annoying blocking artifacts in very low bitarte coding. We propose a delocking filter which is appropriate for real time operation in a conventional video decoder. The proposed algorithm uses on dimensional filtering across block boundaries horizontally and vertiaclly with two separate filtering modes. The mode decision is quite simple but is fully based on the characteristics of human visual system and video sequences. In flat regions, a strong smoothing filter is appliced; and in the other regions, a moew sophisticated smoothing filter, which is based on the frequency information around block boundaries, is used to reduce blocking artifacts without introuducing undesired blur. Eeven though the proposed deblocking filter is quite simple, simulation results show that it improves both subjective and objective image quality for various image features.