• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.60-72
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• 1996

In this paper, a enw model-base three-dimensional (3-D) object recognition mehtod using hopfield network is proposed. To minimize deformation of feature values on 3-D rotation, we select 3-D shape features and 3-D relational features which have rotational invariant characteristics. Then these feature values are normalized to have scale invariant characteristics, also. The input features are matched with model features by optimization process of hopjfield network in the form of two dimensional arrayed neurons. Experimental results on object classification and object matching with the 3-D rotated, scale changed, an dpartial oculued objects show good performance of proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.775-782
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• 2006

We generated blocks from the direction-extracted fingerprint during the pre-process of the fingerprint recognition algorithm and performed training by using the direction minutiae of each block as the input pattern of the neural network, so that we extracted the core points to use in the matching. Based on this, we designed the fingerprint recognition embedded system and tested it using the control board and the serial communication to utilize it for a variety of application systems. As a result, we can verify the reliance satisfactorily.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.6
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• pp.621-632
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• 1989

According to the conventional network evolving towards ISDN, various connection types may be existed in the networks and subscriber-network interfaces. For this reason it is necessary to study the signalling conversion algorithms and the implementation models. In this paper, after surveying the signalling conversion method between the ISDN subscriber and the conventional network, the signaling conversion algorithms are proposed. Based on this algorithms the matching system is designed and developed. Also the validity of this system and the utility of designed software and hardware functions are verified through experiments.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.11b
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• pp.46-51
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• 2001

For Fast Real-time Recognition of Nonlinear Error Data, a new Neural Network algorithm which recognized the map in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion, In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of map, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear map information is processed,

• Journal of Information Processing Systems
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• v.10 no.3
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• pp.471-482
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• 2014

Influence maximization is an important problem of finding a small subset of nodes in a social network, such that by targeting this set, one will maximize the expected spread of influence in the network. To improve the efficiency of algorithm KK_Greedy proposed by Kempe et al., we propose two improved algorithms, Lv_NewGreedy and Lv_CELF. By combining all of advantages of these two algorithms, we propose a mixed algorithm Lv_MixedGreedy. We conducted experiments on two synthetically datasets and show that our improved algorithms have a matching influence with their benchmark algorithms, while being faster than them.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5361-5374
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• 2016

Combining channel coding and network coding in a physical layer in a fading channel, generalized joint channel-network coding (G-JCNC) is proved to highly perform in a two-way relay channel (TWRC). However, most relevant discussions are restricted to symmetric networks. This paper investigates the G-JCNC protocols in an asymmetric TWRC (A-TWRC). A newly designed encoder used by source nodes that is dedicated to correlate codewords with different orders is presented. Moreover, the capability of a simple common non-binary decoder at a relay node is verified. The effects of a power match under various numbers of iteration and code lengths are also analyzed. The simulation results give the optimum power match ratio and demonstrate that the designed scheme based on G-JCNC in an A-TWRC has excellent bit error rate performance under an appropriate power match ratio.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.05b
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• pp.90-95
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• 2003

For the safety driving of an automobile which is become individual requisites, a new Neural Network algorithm which recognized the load vehicles in real time is proposed. The proposed neural network technique is the real time computation method through the inter-node diffusion. In the network, a node corresponds to a state in the quantized input space. Each node is composed of a processing unit and fixed weights from its neighbor nodes as well as its input terminal. The most reliable algorithm derived for real time recognition of vehicles, is a dynamic programming based algorithm based on sequence matching techniques that would process the data as it arrives and could therefore provide continuously updated neighbor information estimates. Through several simulation experiments, real time reconstruction of the nonlinear image information is processed. 1-D LIPN hardware has been composed and various experiments with static and dynamic signals have been implemented.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.347-352
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• 2004

We generated blocks from the direction-extracted fingerprint during the pre-process of the fingerprint recognition algorithm and performed training by using the direction minutiae of each block as the input pattern of the neural network, so that we extracted the core points to use in the matching. Based on this, we designed the fingerprint recognition embedded system and tested it by using the control board and the serial communication to utilize it for a variety of application systems. As a result, we can verify the reliance satisfactorily.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2720-2722
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• 2000

This paper proposes a face detection and recognition method that combines the template matching method and the eigenface method with the neural network. In the face extraction step, the skin color information is used. Therefore, the search region is reduced. The global property of the face is achieved by the eigenface method. Face recognition is performed by a neural network that can learn the face property.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1177-1180
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• 1993

In this paper, a fuzzy-neural interpolating network is proposed to efficiently approximate a nonlinear function. Specifically, basis functions are first constructed by Fuzzy Membership Function based Neural Networks (FMFNN). And the fuzzy similarity, which is defined as the degree of matching between actual output value and the output of each basis function, is employed to determine initial weighting of the proposed network. Then the weightings are updated in such a way that square of the error is minimized. To show the capability of function approximation of the proposed fuzzy-neural interpolating network, a numerical example is illustrated.