• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.330-340
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• 1998

자율주행 반송차가 주어진 경로를 따라 주행 할 때 주행면의 불균일성과 같은 외란요인과 자율반송차 시스템 자체의 비선형성 등으로 인하여 원치 않는 경로추종오차가 발생하게 되는데 본 연구에서는 이러한 경로추종오차를 최소화하기 위해서 신경회로망을 이용한 경로추종 오차 보상방법을 제안한다. 본 방법에서는 신경회로망을 통하여 조향각 보상량을 제공하므로써 경로추종오차를 보상한다. 신경망은 다층 퍼셉트론을 채용하였으며 역전파 알고리즘의 최급강하규칙(Gradient descent rule)을 이용하여 학습을 수행하였다. 본 제안에서는 학습오차를 경로추종오차로부터 정의하므로써 경로추종오차가 최소화되록 신경회로망을 학습시켰다. 제안된 방법의 타당성은 다양한 경로에 대한 모의실험 및 실제 실험을 통하여 검증하였다.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.660-662
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• 1998

데이터 sphering은 신호처리 및 신경망 분야에서 널리 이용되는 기본적인 처리과정으로, 그 목적은 주어진 데이터간에 correlation을 제거하는 것이다. 본 논문에서는 회귀 신경망을 도입하여, 데이터 sphering을 위한 새로운 on-line 알고리듬을 제시한다. 정보 이론에 입각한 risk 함수와, 최근에 제시된 natural gradient을 이용하여 새로운 데이터 sphering 알고리듬을 유도한다. 새로 제시된 알고리듬의 성능을 기존에 널리 이용되어 온 anti-Hebbian 학습 알고리듬과 비교 분석한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1259-1268
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• 2023

This study proposes a throttle and steering control technology using visual sensors based on deep learning's convolutional and recurrent neural networks. It collects camera image and control value data while driving a training track in clockwise and counterclockwise directions, and generates a model to predict throttle and steering through data sampling and preprocessing for efficient learning. Afterward, the model was validated on a test track in a different environment that was not used for training to find the optimal model and compare it with a CNN (Convolutional Neural Network). As a result, we found that the proposed deep learning model has excellent performance.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.1
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• pp.58-63
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• 1999

In this paper, We present a direction indicator algorithm for a mobile robot which uses VFF and neural networks. The structure of this neural network navigation system is composed of sensor system, backpropagation learning controllers for adjusting weight and the motion control system for real-time execution. The experimental results show that the direction indicator system operates properlv and strongly at any circumstance

• The KIPS Transactions:PartB
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• v.14B no.3 s.113
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• pp.215-222
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• 2007

As Information Technology growing, the character recognition application plays an important role in the ubiquitous environment. In this paper, we propose the Modified ART1 network using Multiresolution Mergence to the problems of the character recognition. The approach is based on the unsupervised neural network and multiresolution. In order to decrease noises and to increase the classification rate of the characters, we propose the multiresolution mergence strategy using both high resolution and low resolution information. Also, to maximize the effect of multiresolution mergence, we use a modified ART1 method with a different similarity measure. Our experimental results show that the classification rate of character is quite increased as well as the performance of the propose algorithm in conjunction with the similarity measure is improved comparing to the conventional ART1 algorithm in this application.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.349-350
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• 2019

The autonomous driving drones experimented in the existing indoor corridor environment was a way to give the steering command to the drones by the neural network operation of the notebook due to the limitation of the operation performance of the drones. In this paper, to overcome these limitations, we have studied autonomous driving in indoor corridor environment using NVIDIA Jetson TX2 board.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.47-48
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• 2019

본 논문에서는 신경망의 일종인 자기조직화지도(Self Organizing Map)을 이용하여 컨트롤러의 순서를 정하는 모델을 제안하였다. 자기조직화지도는 자율 학습에 의한 클러스터링을 수행하는 알고리즘으로써 컨트롤러에 가중치를 부여하고 컨트롤러 간 거리를 계산하여 효율적인 컨트롤러 선택을 목표로 한다.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.440-448
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• 2006

Self-Organizing Map (SOM) is an unsupervised learning neural network and it is used for preserving the structural relationships in the data without prior knowledge. SOM has been applied in the study of complex problems such as vector quantization, combinatorial optimization, and pattern recognition. This paper proposes a new usage of SOM as a tool for schema transformation hoping to achieve more efficient genetic process. Every offspring is transformed into an isomorphic neural network with more desirable shape for genetic search. This helps genes with strong epistasis to stay close together in the chromosome. Experimental results showed considerable improvement over previous results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.550-551
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• 2021

Positioning technology is performing important functions in augmented reality, smart factory, and autonomous driving. Among the positioning techniques, the positioning method using beacons has been considered a challenging task due to the deviation of the RSSI value. In this study, the position of a moving object is predicted by training a neural network that takes the RSSI value of the receiver as an input and the distance as the target value. To do this, the measured distance versus RSSI was collected.

• Broadcasting and Media Magazine
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• v.28 no.1
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• pp.13-28
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• 2023

이미지 압축은 이미지 및 영상처리에서 주요한 역할을 하며, 자율주행, 클라우드, 영상 송출 등의 분야에서 빅데이터를 처리해야 하는 수요가 늘어남에 따라 지속적인 연구가 진행 중이다. 그 중심에는 딥러닝(deep learning)의 발전이 자리잡고 있으며, 심층 신경망(deep neural network)을 효과적으로 학습하는 알고리즘들을 적용한 논문들은 기존 압축 포맷인 JPEG, JPEG 2000, MPEG 등의 압축 성능을 뛰어넘는 결과를 보여 주고 있다. 이에 따라 JPEG AI는 딥러닝 기반 학습 이미지 압축의 표준을 제정하는 일을 진행 중이다. 본 기고에서는 JPEG AI가 표준화하고자 하는 기술과 JPEG AI에 제안한 압축 프레임워크들을 분석하고, 활용 사례들을 소개하여 JPEG AI 기반 학습 이미지 압축 모델의 동향에 대해 알아보고자 한다.