• Title/Summary/Keyword: 인공 신경망

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Adoption of Artificial Neural Network for Rest, Enhanced Postprocessing of Beats, and Initial Melody Processing for Automatic Composition System (자동작곡시스템에서 쉼표용 인공신경망 도입 및 개선된 박자후처리와 초기멜로디 처리)

  • Kim, Kyunghwan;Jung, Sung Hoon
    • Journal of Digital Contents Society
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    • v.17 no.6
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    • pp.449-459
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    • 2016
  • This paper proposes a new method to improve the three problems of existing automatic composition method using artificial neural networks. The first problem is that the existing beat post-processing to fit into music theories could not handle all the cases of occurring. The second one is that the pitch space generated by artificial neural networks is distorted because the rest is trained with the pitch on the same neural network with large values. The last problem is caused by the difference between the initial melody and beats given by user and those generated by an artificial neural network in the process of new composition. In order to treat these problems, we propose an enhanced post-processing of beats, initial melody processing, and adoption of artificial neural network for rest. It was found from experiments that the proposed methods totally resolved the three problems.

인공 신경망 기법을 이용한 제지공정의 지절 원인 분석

  • 이진희;이학래
    • Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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    • 2001.04a
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    • pp.168-168
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    • 2001
  • 제지공정의 지절 현상은 많은 공정 변수들이 복합적으로 작용하여 발생하는 가장 큰 공정 트러블 중의 하나이다. 지절은 생산량 감소 뿐만 아니라 발생 후 공정의 복구 와 정리, 생산재가동 및 공정의 재안정화를 위해 많은 시간과 비용, 그리고 노력이 투 입되어야 하므로 공정의 효율과 생산성을 크게 저하시키는 요인이다. 그러나 지절 현상 의 복잡성 때문에 이에 대해 쉽게 접근하거나 해결하지 못하고 있는 것이 현실이지만 그 필요성은 더욱 더 증대되고 있다. 본 연구에서는 최근 들어 각종 산업분야에서 복잡 한 공정상의 결점 발견 및 진단에 효과적이라고 인정받고 있는 예측 분석기법인 인공 신경망(artificial neural network) 시율레이션과 일반적인 통계기법 중의 하나인 주성분 분석을 이용하여 제지 공정의 지절 현상의 검토 가능성을 타진하였다. 인공신경망이란 인간두뇌에서 일어나는 자극-반응-학습과정을 모사하여 현실세계에 존재하는 다양한 현상들의 업력벡터와 출력상태 간의 비선형 mapping올 컴퓨터 시율 레이션을 통하여 분석하고자 하는 기법으로, 여러 가지 현상들을 학습을 통해서 인식하 는 신경망 내의 신경단위들이 병렬처리에 의해 많은 양의 자료에 대한 추론이나 판단 을 신속하고 정확하게 해주는 특징이 있으며 실시간 패턴인식이나 분류 응용분야에도 매우 매력적으로 이용되고 있는 방법이다. 이러한 인공 신경망 기법 중에서도 본 연구 에서는 퍼셉트론의 한계점을 극복하기 위하여 입력총과 출력층에 한 개 이상의 은닉층 ( (hidden layer)을 사용하여 다층 네트워으로 구성하고, 모든 입력패턴에 대하여 발생하 는 오차함수를 최소화하는 방향으로 연결강도를 조정하는 back propagation 학습 알고 리즘을 사용하였다. 지절의 원인으로 추정 가능한 공정인자들을 변수로 하여 최적의 인 공신경망을 구축하기 위해 학습률과 모멘트 상수의 변화 및 은닉층의 수와 출력층의 뉴런 수를 조절하는 동의 작업을 거쳐 네트워크의 정확도가 높은 인공신경망을 설계하 였다. 또한 이러한 인공신경망과의 비교분석을 위해 동일한 공정 데이터들올 이용하여 보편적으로 사용하는 통계기법 중의 하나인 주성분회귀분석을 실시하였다. 주성분 분석은 여러 개의 반응변수에 대하여 얻어진 다변량 자료의 다차원적인 변 수들을 축소, 요약하는 차원의 단순화와 더불어 서로 상관되어있는 반응변수들 상호간 의 복잡한 구조를 분석하는 기법이다. 본 발표에서는 공정 자료를 활용하여 인공신경망 과 주성분분석을 통해 공정 트러블의 발생에 영향 하는 인자들을 보다 현실적으로 추 정하고, 그 대책을 모색함으로써 이를 최소화할 수 있는 방안을 소개하고자 한다.

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Automatic Generation of a Configured Song with Hierarchical Artificial Neural Networks (계층적 인공신경망을 이용한 구성을 갖춘 곡의 자동생성)

  • Kim, Kyung-Hwan;Jung, Sung Hoon
    • Journal of Digital Contents Society
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    • v.18 no.4
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    • pp.641-647
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    • 2017
  • In this paper, we propose a method to automatically generate a configured song with melodies composed of front/middle/last parts by using hierarchical artificial neural networks in automatic composition. In the first layer, an artificial neural network is used to learn an existing song or a random melody and outputs a song after performing rhythm post-processing. In the second layer, the melody created by the artificial neural network in the first layer is learned by three artificial neural networks of front/middle/last parts in the second layer in order to make a configured song. In the artificial neural network of the second layer, we applied a method to generate repeatability using measure identity in order to make song with repeatability and after that the song is completed after rhythm, chord, tonality post-processing. It was confirmed from experiments that our proposed method produced configured songs well.

Site Application of Artificial Neural Network for Tunnel Construction (인공신경망을 이용한 터널시공에서 현장 적용성)

  • Song, Joohyeon;Chae, Hwiyoung;Chun, Byungsik
    • Journal of the Korean GEO-environmental Society
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    • v.13 no.8
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    • pp.25-33
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    • 2012
  • Although it is important to reflect the accurate information of the ground condition in the tunnel design, the analysis and design are conducted by limited information because it is very difficult to consider various geographies and geotechnical conditions. When the tunnel is under construction, examination of accurate safety and prediction of behavior are overcome the limits of predicting behavior by Artificial Neural Network in this study. First, construct the suitable structure after the data of field was made sure by the multi-layer back propagation, then apply with algorithm. Employ the result of measured data from database, and consider the influence factor of tunnel, like supporting pattern, RMR, Q, the types of rock, excavation length, excavation shape, excavation over, to carry out the reliable analysis through field applicability of Artificial Neural Network. After studying, using the ANN model to predict the shearing displacement, convergence displacement, underground displacement, Rock bolt output follow the excavation over of tunnel construction field, then determine the field applicability with ANN through field measured value and comparison analysis when tunnel is being constructed.

Development of Defect Classification Program by Wavelet Transform and Neural Network and Its Application to AE Signal Deu to Welding Defect (웨이블릿 변환과 인공신경망을 이용한 결함분류 프로그램 개발과 용접부 결함 AE 신호에의 적용 연구)

  • Kim, Seong-Hoon;Lee, Kang-Yong
    • Journal of the Korean Society for Nondestructive Testing
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    • v.21 no.1
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    • pp.54-61
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    • 2001
  • A software package to classify acoustic emission (AE) signals using the wavelet transform and the neural network was developed Both of the continuous and the discrete wavelet transforms are considered, and the error back-propagation neural network is adopted as m artificial neural network algorithm. The signals acquired during the 3-point bending test of specimens which have artificial defects on weld zone are used for the classification of the defects. Features are extracted from the time-frequency plane which is the result of the wavelet transform of signals, and the neural network classifier is tamed using the extracted features to classify the signals. It has been shown that the developed software package is useful to classify AE signals. The difference between the classification results by the continuous and the discrete wavelet transforms is also discussed.

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Postprocessing for Tonality and Repeatability, and Average Neural Networks for Training Multiple Songs in Automatic Composition (자동작곡에서 조성과 반복구성을 위한 후처리 방법 및 다수 곡 학습을 위한 평균 신경망 방법)

  • Kim, Kyunghwan;Jung, Sung Hoon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.26 no.6
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    • pp.445-451
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    • 2016
  • This paper introduces a postprocessing method, an iteration method for melody, and an average neural network method for learning a large number of songs in order to improve musically insufficient parts in automatic composition using existing artificial neural network. The melody of songs composed by artificial neural networks is produced according to the melodies of trained songs, so it can not be a specific tonality and it is difficult to have a repetitive composition. In order to solve these problems, we propose a postprocessing method that converts the melody composed by artificial neural networks into a melody having a specific tonality according to music theory and an iteration method for melody by iteratively composing measure divisions of artificial neural networks. In addition, the existing training method of many songs has some disadvantages. To solve this problem, we adopt an average neural network that is made by averaging the weights of artificial neural networks trained each song. From some experiments, it was confirmed that the proposed method solves the existing problems.

Simulation-Based Damage Estimation of Helideck Using Artificial Neural Network (인공 신경망을 사용한 시뮬레이션 기반 헬리데크 손상 추정)

  • Kim, Chanyeong;Ha, Seung-Hyun
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.33 no.6
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    • pp.359-366
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    • 2020
  • In this study, a simulation-based damage estimation method for helidecks is proposed using an artificial neural network. The structural members that share a connecting node in the helideck are regarded as a damage group, and a total of 37,400 damage scenarios are numerically generated by applying randomly assigned damage to up to three damage groups. Modal analysis is then performed for all the damage scenarios, which are selectively used as either training or validation or verification sets based on the purpose of use. An artificial neural network with three hidden layers is constructed using a PyTorch program to recognize the patterns of the modal responses of the helideck model under both damaged and undamaged states, and the network is successively trained to minimize the loss function. Finally, the estimated damage rate from the proposed artificial neural network is compared to the actual assigned damage rate using 400 verification scenarios to show that the neural network is able to estimate the location and amount of structural damage precisely.

Feasibility of Artificial Neural Network Model Application for Evaluation of Undrained Shear Strength from Piezocone Measurements (피에조콘을 이용한 점토의 비배수전단강도 추정에의 인공신경망 이론 적용)

  • 김영상
    • Journal of the Korean Geotechnical Society
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    • v.19 no.4
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    • pp.287-298
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    • 2003
  • The feasibility of using neural networks to model the complex relationship between piezocone measurements and the undrained shear strength of clays has been investigated. A three layered back propagation neural network model was developed based on actual undrained shear strengths, which were obtained from the isotrpoically and anisotrpoically consolidated triaxial compression test(CIUC and CAUC), and piezocone measurements compiled from various locations around the world. It was validated by comparing model predictions with measured values about new piezocone data, which were not previously employed during development of model. Performance of the neural network model was compared with conventional empirical method, direct correlation method, and theoretical method. It was found that the neural network model is not only capable of inferring a complex relationship between piezocone measurements and the undrained shear strength of clays but also gives a more precise and reliable undrained shear strength than theoretical and empirical approaches. Furthermore, neural network model has a possibility to be a generalized relationship between piezocone measurements and undrained shear strength over the various places and countries, while the present empirical correlations present the site specific relationship.

Evolving Team-Agent Based on Dynamic State Evolutionary Artificial Neural Networks (동적 상태 진화 신경망에 기반한 팀 에이전트의 진화)

  • Jin, Xiang-Hua;Jang, Dong-Heon;Kim, Tae-Yong
    • Journal of Korea Multimedia Society
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    • v.12 no.2
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    • pp.290-299
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    • 2009
  • Evolutionary Artificial Neural Networks (EANNs) has been highly effective in Artificial Intelligence (AI) and in training NPCs in video games. When EANNs is applied to design game NPCs' smart AI which can make the game more interesting, there always comes two important problems: the more complex situation NPCs are in, the more complex structure of neural networks needed which leads to large operation cost. In this paper, the Dynamic State Evolutionary Neural Networks (DSENNs) is proposed based on EANNs which deletes or fixes the connection of the neurons to reduce the operation cost in evolution and evaluation process. Darwin Platform is chosen as our test bed to show its efficiency: Darwin offers the competitive team game playing behaviors by teams of virtual football game players.

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Prediction of Water Quality in Large Rivers with Tributary Input using Artificial Neural Network Model (인공신경망 모델을 이용한 지천유입이 있는 대하천의 수질예측)

  • Seo, Il Won;Yun, Se Hun;Jung, Sung Hyun
    • Proceedings of the Korea Water Resources Association Conference
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    • 2018.05a
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    • pp.45-45
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    • 2018
  • 오염물의 혼합거동을 해석하기 위해 물리기반 모델을 이용하는 경우 모델을 구축하고 운용하는데 많은 시간과 재정이 소요되며 현장검증을 통한 검증이 반드시 필요하다. 하지만 데이터 기반 모델의 경우 축적된 데이터만으로도 예측을 수행할 수 있으며 물리기반모델에 비해 결정해야할 입력인자가 적어 모델운용이 용이하다는 장점이 있다. 다양한 데이터 모델 중 인공신경망(ANN) 모델은 데이터가 가지는 불확실성 및 비정상성, 복잡한 상호관련성에 효과적으로 대응할 수 있는 모델로 수자원 및 환경 분야에서 자주 사용되고 있다. 본 연구에서는 인공신경망 모델을 이용하여 지천유입이 있는 대하천의 수질인자 (pH, 전기전도도, DO, chl-a)를 예측하였다. 다른 데이터기반 모델과 같이 인공신경망 모델 또한 수집된 데이터 질에 크게 영향을 받으며, 내부 입력인자의 선택이 모델의 예측 결과에 큰 영향을 미친다. 이러한 인공신경망 모델의 특성을 바탕으로 예측모형의 정확도를 향상하기 위해서는 크게 데이터 처리부분과 모델구축 부분에서의 접근이 필요하다. 본 연구에서는 데이터 처리 과정에서 연구대상지점의 각각의 수질인자가 가지는 분포 특성을 유지하기 위해 층화표츨추출법을 이용하여 데이터를 구성하였다. 모델의 구축 과정에서는 초기가중치 값의 영향을 줄이기 위해 앙상블기법을 사용하였으며, 좀 더 견고하고 정확한 결과를 예측하기 위해 탄력적 역전파알고리즘을 추가하였다. 추가적으로 합류 후 본류의 미 계측지역 수질 예측 정확도 향상을 위해 본류의 수질인자뿐만 아니라 지류의 수질인자를 입력자료로 사용하여 모의를 수행하였다. 또한 동일 구간에서 수행한 현장추적자실험 자료를 이용하여 수질인자의 분포특성을 비교, 검증하였다. 개발된 모델을 이용하여 낙동강과 금호강 합류부 하류의 수질인자를 예측한 결과 지류의 수질인자를 입력자료로 추가한 경우 예측의 정확도가 증가하였으며, 현장실험 자료를 통해 밝혀진 오염물의 거동현상을 인공신경망 모델로도 동일하게 재현하는 것으로 나타났다. 본 연구에서 제안한 인공신경모델을 이용한다면 물리기반 수치모델을 대체하여 지천으로 유입된 오염물의 거동을 정확하고 효율적으로 파악할 수 있을 것이다.

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