• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.417-420
• /
• 2014

Importance of security system to prevent recently increased financial security accident is increasing. Biometric system between the security systems is focused. Fingerprint recognition has many useful aspects such as security, reliability and portability. In this treatise, fingerprint recognition technique is realized by using artificial neural network. Artificial Neural Network(ANN) is a mathematics learning model that makes specific patterns that a program can recognize to show a nerve network's characteristic on a computer. Input fingerprint images have a preprocessing process such as equalization, binarization and thinning. We extract minutiae feature in the images and program can recognize a fingerprint through ANN.

• Computational Structural Engineering
• /
• v.9 no.2
• /
• pp.93-101
• /
• 1996

An artificial neural network is a computational model that mimics the biological system of the brain and it consists of a number of interconnected processing units where it can reasonably infer by them. Because the neural network is particularly useful for evaluating systems with a multitude of nonlinear variables, it can be used in experimental results predictions, in structural planning and in optimum design of structures. This paper describes the basic theory related to the neural networks and discusses the applicability of neural networks to predict the ultimate shear capacity of the precast concrete vertical joints by comparing the neural networks with a conventional method such as regression.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.251-254
• /
• 2001

본 논문에서는 Self Organizing Map을 이용하여 fMRI data를 분석해 보았다. fMRl (functional Magnetic Resonance Imaging)는 인간의 뇌에 대한 비 침투적 연구 방법 중 최근에 각광받고 있는 것이다. Motor task를 수행하고 있는 피험자로부터 image data를 얻어내어 SOM을 적용하여 clustering한 결과 motor cortex 영역이 뚜렷하게 clustering 되었음을 알 수 있었다.

• The Magazine of the IEIE
• /
• v.42 no.2
• /
• pp.45-56
• /
• 2015

• Journal of the korean academy of Pediatric Dentistry
• /
• v.23 no.3
• /
• pp.582-592
• /
• 1996

본 실험은 삼차신경 자극으로 발생되는 체성 감각 유발 전위에 대한 국소마취제의 효과를 관찰하였다. 나트륨 통로차단을 통하여 약리작용을 나타내는 것으로 알려져 있는 리도카인를 뇌 피질에 국소 투여한 후 삼차신경의 체성 감각유발 전위의 강도및 지연시간을 측정하였다. 케타민으로 마취된 흰쥐의 대측성 구레나룻 자극후 뇌의 체성 감각영역으로부터 기록되는 유발전위를 분석한 결과, 리도카인을 뇌 피질에 국소 투여시 유발전위의 강도 및 지연시간의 감소가 나타났으며, 필드 전위의 형태는 이상성 (양극성 및 음극성) 혹은 삼상성 (양극성, 음극성 및 양극성) 의 파형으로 나타났다. 필드 전위의 발생 부위는 뇌 피질의 중대뇌동맥의 상행지 상방영역이었다. 본 실험에서 나타난 초기 전위변동은 피질판 상층에 존재하는 신경세포의 탈분극 과청에 의하여 생성되고 후기의 전위 변동은 동일 영역의 하층 신경세포에서 과분극 혹은 재분극이 발생한 결과라고 유추된다. 따라서 삼차신경계의 체성 감각 영역에서는 피질 상층및 하층의 과립성 피라미드 세포의 순차적인 활성화에 의하여 기본적인 신경 회로망이 형성되어 있으며 생리적 자극으로 유발되는 필드 전위는 이러한 신경망를 통하여 발생될 것으로 사료된다.

• Journal of Korean Society of Steel Construction
• /
• v.16 no.4 s.71
• /
• pp.443-452
• /
• 2004

Recently, the Artificial Neural Network(ANN) which can organize complex non-linear problems by effectively applying the parallel computational model that is similar to the human brain, was adopted in the wide department of technology and resulted in many successful applications. In this study, a more appropriate formal method is suggested for the preliminary structural design stage controlled merely by the designer's experience and intuition. To do so, this study proposes a multi-level ANN according to the general progressive structural design procedure, using Back-Propagation Algorithm (BP) and Genetic Algorithm (GA) for the ANN learning. The preliminary structural design of cable-stayed bridges was applied to illustrate the applicability of the study formulated as stated above, and the results of two different learning methods were compared.

• Journal for History of Mathematics
• /
• v.22 no.3
• /
• pp.31-44
• /
• 2009

Anyone wishing to understand cognitive science, a converging science, need to become familiar with three major mathematical landmarks: Turing machines, Neural networks, and $G\ddot{o}del's$ incompleteness theorems. The present paper aims to explore the mathematical foundations of cognitive science, focusing especially on these historical landmarks. We begin by considering cognitive science as a metamathematics. The following parts addresses two mathematical models for cognitive systems; Turing machines as the computer system and Neural networks as the brain system. The last part investigates $G\ddot{o}del's$ achievements in cognitive science and its implications for the future of cognitive science.

• The Journal of the Acoustical Society of Korea
• /
• v.40 no.3
• /
• pp.228-233
• /
• 2021

In this paper, we propose a method to extract and classify features of multi-channel ElectroEncephalo Graphy (EEG) that change according to various musical tempo stimuli. In the proposed method, a 3D convolutional bidirectional gated recurrent neural network extracts spatio-temporal and long time-dependent features from the 3D EEG input representation transformed through the preprocessing. The experimental results show that the proposed tempo stimuli classification method is superior to the existing method and the possibility of constructing a music-based brain-computer interface.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.1
• /
• pp.178-186
• /
• 1996

We develop a simple adaptive population coding model for neural networks based upon an error minimization method. Our model exhibits properties that have been experimentally observed in the population coding of the motor-cortical cells during the voluntary arm movements of primates. By removing a group of directionally tuned cells after learning, we study its contribution to the population coding. Through the learning process of the remained cells, we observe how the cells modify their preferred directions to reduce the coding errors. Since this adaptive property has been neither predicted nor experimentally observed before, it would be interesting to find whether a similar adaptive property exists in real cortices that are believed to encode the information in their cell populations.

• Investigative Magnetic Resonance Imaging
• /
• v.15 no.2
• /
• pp.110-122
• /
• 2011

Purpose : The purpose of this study is to establish the method generating human brain anatomical connectivity from Korean children and evaluating the network topological properties using small-world network analysis. Materials and Methods : Using diffusion tensor images (DTI) and parcellation maps of structural MRIs acquired from twelve healthy Korean children, we generated a brain structural connectivity matrix for individual. We applied one sample t-test to the connectivity maps to derive a representative anatomical connectivity for the group. By spatially normalizing the white matter bundles of participants into a template standard space, we obtained the anatomical brain network model. Network properties including clustering coefficient, characteristic path length, and global/local efficiency were also calculated. Results : We found that the structural connectivity of Korean children group preserves the small-world properties. The anatomical connectivity map obtained in this study showed that children group had higher intra-hemispheric connectivity than inter-hemispheric connectivity. We also observed that the neural connectivity of the group is high between brain stem and motorsensory areas. Conclusion : We suggested a method to examine the anatomical brain network of Korean children group. The proposed method can be used to evaluate the efficiency of anatomical brain networks in people with disease.