• Title/Summary/Keyword: neural network learning

Search Result 4,098, Processing Time 0.031 seconds

Impact parameter prediction of a simulated metallic loose part using convolutional neural network

  • Moon, Seongin;Han, Seongjin;Kang, To;Han, Soonwoo;Kim, Kyungmo;Yu, Yongkyun;Eom, Joseph
    • Nuclear Engineering and Technology
    • /
    • v.53 no.4
    • /
    • pp.1199-1209
    • /
    • 2021
  • The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.

Model Transformation and Inference of Machine Learning using Open Neural Network Format (오픈신경망 포맷을 이용한 기계학습 모델 변환 및 추론)

  • Kim, Seon-Min;Han, Byunghyun;Heo, Junyeong
    • The Journal of the Institute of Internet, Broadcasting and Communication
    • /
    • v.21 no.3
    • /
    • pp.107-114
    • /
    • 2021
  • Recently artificial intelligence technology has been introduced in various fields and various machine learning models have been operated in various frameworks as academic interest has increased. However, these frameworks have different data formats, which lack interoperability, and to overcome this, the open neural network exchange format, ONNX, has been proposed. In this paper we describe how to transform multiple machine learning models to ONNX, and propose algorithms and inference systems that can determine machine learning techniques in an integrated ONNX format. Furthermore we compare the inference results of the models before and after the ONNX transformation, showing that there is no loss or performance degradation of the learning results between the ONNX transformation.

A study on sequential iterative learning for overcoming catastrophic forgetting phenomenon of artificial neural network (인공 신경망의 Catastrophic forgetting 현상 극복을 위한 순차적 반복 학습에 대한 연구)

  • Choi, Dong-bin;Park, Young-beom
    • Journal of Platform Technology
    • /
    • v.6 no.4
    • /
    • pp.34-40
    • /
    • 2018
  • Currently, artificial neural networks perform well for a single task, but NN have the problem of forgetting previous learning by learning other kinds of tasks. This is called catastrophic forgetting. To use of artificial neural networks in general purpose this should be solved. There are many efforts to overcome catastrophic forgetting. However, even though there was a lot of effort, it did not completely overcome the catastrophic forgetting. In this paper, we propose sequential iterative learning using core concepts used in elastic weight consolidation (EWC). The experiment was performed to reproduce catastrophic forgetting phenomenon using EMNIST data set which extended MNIST, which is widely used for artificial neural network learning, and overcome it through sequential iterative learning.

A Study on Intelligent Control of Real-Time Working Motion Generation of Bipped Robot (2족 보행로봇의 실시간 작업동작 생성을 위한 지능제어에 관한 연구)

  • Kim, Min-Seong;Jo, Sang-Young;Koo, Young-Mok;Jeong, Yang-Gun;Han, Sung-Hyun
    • Journal of the Korean Society of Industry Convergence
    • /
    • v.19 no.1
    • /
    • pp.1-9
    • /
    • 2016
  • In this paper, we propose a new learning control scheme for various walk motion control of biped robot with same learning-base by neural network. We show that learning control algorithm based on the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multi layer back propagation neural network identification is simulated to obtain a dynamic model of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The biped robots have been received increased attention due to several properties such as its human like mobility and the high-order dynamic equation. These properties enable the biped robots to perform the dangerous works instead of human beings. Thus, the stable walking control of the biped robots is a fundamentally hot issue and has been studied by many researchers. However, legged locomotion, it is difficult to control the biped robots. Besides, unlike the robot manipulator, the biped robot has an uncontrollable degree of freedom playing a dominant role for the stability of their locomotion in the biped robot dynamics. From the simulation and experiments the reliability of iterative learning control was illustrated.

A Note for Speed-Up of Interval Regression Neural Network (구간회귀 신경망의 속도개선)

  • 이중우;권순학
    • Proceedings of the Korean Institute of Intelligent Systems Conference
    • /
    • 2001.05a
    • /
    • pp.101-104
    • /
    • 2001
  • This paper deals with the speed-up of interval regression neural network. We propose an improved method of adjusting the parameter alpha used in the interval regression neural network to improve the learning speed and regression performance. Finally, we provide numerical examples to evaluate the performance of the proposed method.

  • PDF

The Azimuth and Velocity Control of a Mobile Robot with Two Drive Wheels by Neural-Fuzzy Control Method (뉴럴-퍼지제어기법에 의한 두 구동휠을 갖는 이동형 로보트의 자세 및 속도 제어)

  • Cho, Y.G.;Bae, J.I.
    • Journal of Power System Engineering
    • /
    • v.2 no.3
    • /
    • pp.74-82
    • /
    • 1998
  • This paper presents a new approach to the design of speed and azimuth control of a mobile robot with two drive wheels. The proposed control scheme uses a Gaussian function as a unit function in the neural-fuzzy network and back propagation algorithm to train the neural-fuzzy network controller in the framework of the specialized learning architecture. It is proposed to a learned controller with two neural-fuzzy networks based on an independent reasoning and a connection net with fixed weights to simplify the neural-fuzzy network. The performance of the proposed controller can be seen by the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

  • PDF

Development of the Neural Network Steering Controller for Unmanned electric Vehicle (무인 전기자동차의 신경회로망 조향 제어기 개발)

  • 손석준;김태곤;김정희;류영재;김의선;임영철;이주상
    • Proceedings of the Korean Institute of Intelligent Systems Conference
    • /
    • 2000.11a
    • /
    • pp.281-286
    • /
    • 2000
  • This paper describes a lateral guidance system of an unmanned vehicle, using a neural network model of magneto-resistive sensor and magnetic fields. The model equation was compared with experimental sensing data. We found that the experimental result has a negligible difference from the modeling equation result. We verified that the modeling equation can be used in the unmanned vehicle simulations. As the neural network controller acquires magnetic field values(B$\_$x/, B$\_$y/, B$\_$z/) from the three-axis, the controller outputs a steering angle. The controller uses the back-propagation algorithms of neural network. The learning pattern acquisition was obtained using computer simulation, which is more exact than human driving. The simulation program was developed in order to verify the acquisition of the learning pattern, learning itself, and the adequacy of the design controller. A computer simulation of the vehicle (including vehicle dynamics and steering) was used to verify the steering performance of the vehicle controller using the neural network. Good results were obtained. Also, the real unmanned electrical vehicle using neural network controller verified good results.

  • PDF

A Study on Fuzzy Wavelet Neural Network System Based on ANFIS Applying Bell Type Fuzzy Membership Function (벨형 퍼지 소속함수를 적용한 ANFIS 기반 퍼지 웨이브렛 신경망 시스템의 연구)

  • 변오성;조수형;문성용
    • Journal of the Institute of Electronics Engineers of Korea TE
    • /
    • v.39 no.4
    • /
    • pp.363-369
    • /
    • 2002
  • In this paper, it could improved on the arbitrary nonlinear function learning approximation which have the wavelet neural network based on Adaptive Neuro-Fuzzy Inference System(ANFIS) and the multi-resolution Analysis(MRA) of the wavelet transform. ANFIS structure is composed of a bell type fuzzy membership function, and the wavelet neural network structure become composed of the forward algorithm and the backpropagation neural network algorithm. This wavelet composition has a single size, and it is used the backpropagation algorithm for learning of the wavelet neural network based on ANFIS. It is confirmed to be improved the wavelet base number decrease and the convergence speed performances of the wavelet neural network based on ANFIS Model which is using the wavelet translation parameter learning and bell type membership function of ANFIS than the conventional algorithm from 1 dimension and 2 dimension functions.

Speech Recognition and Its Learning by Neural Networks (신경회로망을 이용한 음성인식과 그 학습)

  • 이권현
    • The Journal of Korean Institute of Communications and Information Sciences
    • /
    • v.16 no.4
    • /
    • pp.350-357
    • /
    • 1991
  • A speech recognition system based on a neural network, which can be used for telephon number services was tested. Because in Korea two different cardinal number systems, a koreanic one and a sinokoreanic one, are in use, it is necessary that the used systems is able to recognize 22 discret words. The structure of the neural network used had two layers, also a structure with 3 layers, one hidden layreformed of each 11, 22 and 44 hidden units was tested. During the learning phase of the system the so called BP-algorithm (back propagation) was applied. The process of learning can e influenced by using a different learning factor and also by the method of learning(for instance random or cycle). The optimal rate of speaker independent recognition by using a 2 layer neural network was 96%. A drop of recognition was observed by overtraining. This phenomen appeared more clearly if a 3 layer neural network was used. These phenomens are described in this paper in more detail. Especially the influence of the construction of the neural network and the several states during the learning phase are examined.

  • PDF

Real-time Artificial Neural Network for High-dimensional Medical Image (고차원 의료 영상을 위한 실시간 인공 신경망)

  • Choi, Kwontaeg
    • Journal of the Korean Society of Radiology
    • /
    • v.10 no.8
    • /
    • pp.637-643
    • /
    • 2016
  • Due to the popularity of artificial intelligent, medical image processing using artificial neural network is increasingly attracting the attention of academic and industry researches. Deep learning with a convolutional neural network has been proved to very effective representation of images. However, the training process requires high performance H/W platform. Thus, the realtime learning of a large number of high dimensional samples within low-power devices is a challenging problem. In this paper, we attempt to establish this possibility by presenting a realtime neural network method on Raspberry pi using online sequential extreme learning machine. Our experiments on high-dimensional dataset show that the proposed method records an almost real-time execution.