• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.1228-1230
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• 1996

A neural network(NN) mechanism is proposed to modify the gait of a biped robot that walks on sloping surface using sensory inputs. The robot starts walking on a surface with no priori knowledge of the inclination of the surface. By accumulating experience during walking, the robot improves its walking gait and finally forms a gait that is adapted to the surface inclination. A neural controller is proposed to control the gait which has 72 reciprocally inhibited and excited neurons. PI control is used for position control, and the neurons are trained by a reinforcement learning mechanism. Experiments of static gait learning and pseudo dynamic learning are performed to show the validity of the proposed reinforcement learning mechanism.

• Journal of Information Processing Systems
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• 제18권2호
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• pp.188-196
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• 2022

Recently, neural architecture search (NAS) has received increasing attention as it can replace human experts in designing the architecture of neural networks for different tasks and has achieved remarkable results in many challenging tasks. In this study, a path-based computation neural architecture encoder (PCE) was proposed. Our PCE first encodes the computation of information on each path in a neural network, and then aggregates the encodings on all paths together through an attention mechanism, simulating the process of information computation along paths in a neural network and encoding the computation on the neural network instead of the structure of the graph, which is more consistent with the computational properties of neural networks. We performed an extensive comparison with eight encoding methods on two commonly used NAS search spaces (NAS-Bench-101 and NAS-Bench-201), which included a comparison of the predictive capabilities of performance predictors and search capabilities based on two search strategies (reinforcement learning-based and Bayesian optimization-based) when equipped with different encoders. Experimental evaluation shows that PCE is an efficient encoding method that effectively ranks and predicts neural architecture performance, thereby improving the search efficiency of neural architectures.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.218-223
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• 1992

This paper describes a learning method of neural network controllers. The learning method improves the performance of indirect learning mechanism in the neuro-control of nonlinear systems. To precisely identify dynamic characteristics of the plant by utilizing a limited prior information we propose a new energy function which takes advantage of the proportional relationship between outputs of the plant and those of neural networks.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2003년도 춘계종합학술대회
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• pp.473-476
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• 2003

오늘날 인터넷은 하나의 거대한 분산 정보 서비스센터의 역할을 수행하며 여러 가지 많은 정보들과 이를 관리 운영하는 데이터 베이스 서버들은 분산된 네트워크 환경 속에서 광범위하게 존재하고 있다. 그러나 우리는 데이터 특성에 따라 입력 데이터를 처리할 서버를 결정하는데 여러 가지 어려움을 겪고 있다. 본 논문에서는 분산 환경 속에 존재하는 수많은 데이터들 가운데 신경망을 이용해 입력 데이터 패턴을 가장 효율적으로 처리할 수 있는 목적지 서버를 마이닝하는 기법과 이를 기반으로 한 지능적 데이터 마이닝 시스템 구조를 설계하였다. 그 결과로서 새로운 입력 데이터패턴이 신경망으로 구현된 동적 바인딩 방법에 따라 목적지 서버를 결정한 후 처리됨을 보였다. 이 기법은 데이터 웨어하우스, 통신 및 전력부하패턴 분석, 인구센서스 분석, 의료데이터 분석에 활용될 수 있다.

• Journal of Korean Neurosurgical Society
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• 제63권3호
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• pp.327-337
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• 2020

Junctional neurulation represents the most recent adjunct to the well-known sequential embryological processes of primary and secondary neurulation. While its exact molecular processes, occurring at the end of primary and the beginning of secondary neurulation, are still being actively investigated, its pathological counterpart -junctional neural tube defect (JNTD)- had been described in 2017 based on three patients whose well-formed secondary neural tube, the conus, is widely separated from its corresponding primary neural tube and functionally disconnected from corticospinal control from above. Several other cases conforming to this bizarre neural tube arrangement have since appeared in the literature, reinforcing the validity of this entity. The cardinal clinical, neuroimaging, and electrophysiological features of JNTD, and the hypothesis of its embryogenetic mechanism, form part of this review.

• International Journal of Aeronautical and Space Sciences
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• 제16권4호
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• pp.510-519
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• 2015

Concerning the issue of high-dimensions, hybrid uncertainties of randomness and intervals including implicit and highly nonlinear limit state function, reliability analysis based on the hybrid uncertainty reliability mode combining with back propagation neural network (HU-BP neural network) is proposed in this paper. Random variables and interval variables are as input layer of the neural network, after the training and approximation of the neural network, the response variables are obtained through the output layer. Reliability index is calculated by solving the optimization model of the most probable point (MPP) searching in the limit state band. Two numerical cases are used to demonstrate the method proposed in this paper, and finally the method is employed to solving an engineering problem of the aerospace friction plate. For this high nonlinear, small failure probability problem with interval variables, this method could achieve a good analysis result.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권3호
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• pp.176-181
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• 2007

This paper presents evolvable neural networks based on a developmental model for navigation control of autonomous mobile robots in dynamic operating environments. Bio-inspired mechanisms have been applied to autonomous design of artificial neural networks for solving practical problems. The proposed neural network architecture is grown from an initial developmental model by a set of production rules of the L-system that are represented by the DNA coding. The L-system is based on parallel rewriting mechanism motivated by the growth models of plants. DNA coding gives an effective method of expressing general production rules. Experiments show that the evolvable neural network designed by the production rules of the L-system develops into a controller for mobile robot navigation to avoid collisions with the obstacles.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2008년도 추계학술대회 논문집
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• pp.491-492
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• 2008

• 디지털콘텐츠학회 논문지
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• 제19권6호
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• pp.1161-1167
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• 2018

기계 학습의 발전은 인간만이 할 수 있었던 섬세한 작업들을 기계가 할 수 있도록 이끌었고, 이에 따라 많은 기업체들은 기계 학습 기반의 번역기를 출시하였다. 현재 상용화된 번역기들은 우수한 성능을 보이지만 숫자 번역에서 문제가 발생하는 것을 발견했다. 번역기들은번역할문장에 큰숫자가 있을경우종종숫자를잘못번역하며, 같은문장에서숫자만바꿔번역할 때문장의구조를 완전히바꾸어 번역하기도 한다. 이러한 문제점은오번역의 가능성을 높이기 때문에해결해야 될 사안으로여겨진다. 본 논문에서는 Bidirectional RNN (Recurrent Neural Network), LSTM (Long Short Term Memory networks), Attention mechanism을 적용한 Neural Machine Translation 모델을 사용하여 데이터 클렌징, 사전 크기 변경을 통한 모델 최적화를 진행 하였고, 최적화된 모델에 숫자 기호화 알고리즘을 적용하여 상기 문제점을 해결하는 번역 시스템을 구현하였다. 본논문은 데이터 클렌징 방법과 사전 크기 변경, 그리고 숫자 기호화 알고리즘에 대해 서술하였으며, BLEU score (Bilingual Evaluation Understudy score) 를 이용하여 각 모델의 성능을 비교하였다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.547-550
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• 2009

The object recognition mechanism of human being is not well understood yet. On research of animal experiment using an ape, however, neurons that respond to simple shape (e.g. circle, triangle, square and so on) were found. And Hypothesis has been set up as human being may recognize object as combination of such simple shapes. That mechanism is called Figure Alphabet Hypothesis, and those simple shapes are called Figure Alphabet. As one way to research object recognition algorithm, we focused attention to this Figure Alphabet Hypothesis. Getting idea from it, we proposed the feature extraction algorithm for object recognition. In this paper, we described recognition of binarized images of multifont alphabet characters by the recognition model which combined three-layered neural network in the feature extraction algorithm. First of all, we calculated the difference between the learning image data set and the template by the feature extraction algorithm. The computed finite difference is a feature quantity of the feature extraction algorithm. We had it input the feature quantity to the neural network model and learn by backpropagation (BP method). We had the recognition model recognize the unknown image data set and found the correct answer rate. To estimate the performance of the contriving recognition model, we had the unknown image data set recognized by a conventional neural network. As a result, the contriving recognition model showed a higher correct answer rate than a conventional neural network model. Therefore the validity of the contriving recognition model could be proved. We'll plan the research a recognition of natural image by the contriving recognition model in the future.