• 전자공학회논문지C
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• 제36C권11호
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• pp.75-83
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• 1999

주어진 응용에 적합한 MLP 분류기의 위상 구조를 효율적으로 학습하기 위하여 종족 유전 알고리즘 (SGA)과 파라미터 프리 유전 알고리즘(PfGA)을 결합한 파라미터 프리 종족 유전 알고리즘(PfSGA)을 제안한다. SGA는 전체 탐색 공간을 은닉층 뉴런의 수에 따라 여러 개의 영역(종족)으로 분할한 후, 학습 과정 중 기준에 미달되는 종족에 대해서는 학습을 중단시킴으로써 불필요한 탐색을 줄이는 학습법이다. 그러나 SGA는 돌연변이나 교배 확률 등 학습 파라미터 설정에 따라 분류기의 성능이 달라진다. 따라서 이 논문에서는 SGA와 PfGA를 결합하여 파라미터 설정에 무관하도록 하였다. 벤치마크 데이터와 수화 단어에 대하여 실험한 결과 PfSGA는 기존의 SGA에 비해 학습 시간을 단축시킬 수 있으며, 파라미터의 설정에 영향을 받지 않았다. 또한 기존의 방법에 비해 오인식율과 위상 구조 등에 있어서 효율적임을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.120-123
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• 1997

This paper presents models of fuzzy inference systems that can be built from a set of input-output training data pairs through hybrid structure-parameter learning. Fuzzy inference systems has the difficulty of parameter learning. Here we develop a coding format to determine a fuzzy neural network(FNN) model by chromosome in a genetic algorithm(GA) and present systematic approach to identify the parameters and structure of FNN. The proposed FNN can automatically identify the fuzzy rules and tune the membership functions by modifying the connection weights of the networks using the GA and the back-propagation learning algorithm. In order to show effectiveness of it we simulate and compare with conventional methods.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.319-323
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• 1994

In this paper, we discuss a design method of iterative learning control systems for parabolic linear distributed parameter systems(DPSs). First, we discuss some aspects of boundary control of the DPS, and then propose to employ the Karhunen-Loeve procedure to reduce the infinite dimensional problem to a low-order finite dimensional problem. An iterative learning control(ILC) for non-square transfer function matrix is introduced finally for the reduced order system.

• International Journal of Advanced Culture Technology
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• 제9권4호
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• pp.295-301
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• 2021

Digital media technology is gradually developing with the development of convergence quaternary industrial technology and mobile devices. The combination of deep learning and augmented reality can provide more convenient and lively services through the interaction of 3D virtual images with the real world. We combine deep learning-based pose prediction with augmented reality technology. We predict the eight vertices of the bounding box of the object in the image. Using the predicted eight vertices(x,y), eight vertices(x,y,z) of 3D mesh, and the intrinsic parameter of the smartphone camera, we compute the external parameters of the camera through the PnP algorithm. We calculate the distance to the object and the degree of rotation of the object using the external parameter and apply to AR content. Our method provides services in a web environment, making it highly accessible to users and easy to maintain the system. As we provide augmented reality services using consumers' smartphone cameras, we can apply them to various business fields.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1387-1392
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• 1990

In this paper, an iterative learning control algorithm for unknown linear discrete systems is proposed by employing a parameter estimator together with an inverse system model. Regardless of initial error and inherent parameter uncertainty, a good tracking control performance is obtained using the proposed learning control algorithm characterized by recursive operations. A sufficient condition for convergency is provided to show the effectiveness of the proposed algorithm. To investigate the performance of the algorithm a series of simulations and experiments were performed for the tracking control of a servo motor.

• International journal of advanced smart convergence
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• 제7권1호
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• pp.24-32
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• 2018

An outlier detection method using mixed prediction model has been described in this paper. The mixed prediction model consists of time-series model and regression model. The parameter estimation of the prediction model was performed using supervised learning and a genetic algorithm is adopted for a learning method. The experiments were performed in artificial and real data set. The prediction performance is compared with the existing prediction methods using artificial data. Outlier detection is conducted using the real sensor measurements in a dam. The validity of the proposed method was shown in the experiments.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1864-1866
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• 1991

We present a parameter estimator which operates in the domain of iteration sequence. The scheme can be applied to identify unknown algebraic system whose uncertainty is parametric.

• 한국정보통신학회논문지
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• 제24권10호
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• pp.1341-1347
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• 2020

부정맥 분류를 위한 기존 연구들은 분류의 정확성을 높이기 위해 신경회로망(Artificial Neural Network), 기계학습(Machine Learning) 등을 이용한 방법이 연구되어 왔다. 특히 딥러닝은 신경회로망의 문제인 은닉층 개수의 한계를 해결함으로 인해 인공 지능 기반의 부정맥 분류에 많이 사용되고 있다. 본 연구에서는 AR 기반의 특징점 추출과 딥러닝을 통한 부정맥 분류 방법을 제안한다. 이를 위해 먼저 잡음을 제거한 ECG 신호에서 R파를 검출하고 자기 회귀 모델을 통하여 최적의 QRS와 RR간격을 추출하였다. 이후 딥러닝을 통한 지도학습 방법으로 가중치를 학습시키고 부정맥을 분류하였다. 제안된 방법의 타당성 평가를 위해 MIT-BIH 부정맥 데이터베이스를 통해 각 파라미터에 따른 훈련 및 분류 정확도를 확인하였다. 성능 평가 결과 PVC는 약 97% 이상의 평균 분류율을 나타내었다.

• 치위생과학회지
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• 제20권4호
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• pp.206-212
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• 2020

Background: A Teachable Machine is a kind of machine learning web-based tool for general persons. In this paper, the feasibility of Google's Teachable Machine (ver. 2.0) was studied in the diagnosis of the tooth-marked tongue. Methods: For machine learning of tooth-marked tongue diagnosis, a total of 1,250 tongue images were used on Kaggle's web site. Ninety percent of the images were used for the training data set, and the remaining 10% were used for the test data set. Using Google's Teachable Machine (ver. 2.0), machine learning was performed using separated images. To optimize the machine learning parameters, I measured the diagnosis accuracies according to the value of epoch, batch size, and learning rate. After hyper-parameter tuning, the ROC (receiver operating characteristic) analysis method determined the sensitivity (true positive rate, TPR) and specificity (false positive rate, FPR) of the machine learning model to diagnose the tooth-marked tongue. Results: To evaluate the usefulness of the Teachable Machine in clinical application, I used 634 tooth-marked tongue images and 491 no-marked tongue images for machine learning. When the epoch, batch size, and learning rate as hyper-parameters were 75, 0.0001, and 128, respectively, the accuracy of the tooth-marked tongue's diagnosis was best. The accuracies for the tooth-marked tongue and the no-marked tongue were 92.1% and 72.6%, respectively. And, the sensitivity (TPR) and specificity (FPR) were 0.92 and 0.28, respectively. Conclusion: These results are more accurate than Li's experimental results calculated with convolution neural network. Google's Teachable Machines show good performance by hyper-parameters tuning in the diagnosis of the tooth-marked tongue. We confirmed that the tool is useful for several clinical applications.

• 천문학회보
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• 제41권2호
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• pp.54.2-54.2
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• 2016

We present a machine learning approach to estimating stellar atmospheric parameters, effective temperature (Teff), surface gravity (log g), and metallicity ([Fe/H]) for stars observed during the course of the Sloan Digital Sky Survey (SDSS). For training a neural network, we randomly sampled the SDSS data with stellar parameters available from SEGUE Stellar Parameter Pipeline (SSPP) to cover the parameter space as wide as possible. We selected stars that are not included in the training sample as validation sample to determine the accuracy and precision of each parameter. We also divided the training and validation samples into four groups that cover signal-to-noise ratio (S/N) of 10-20, 20-30, 30-50, and over 50 to assess the effect of S/N on the parameter estimation. We find from the comparison of the network-driven parameters with the SSPP ones the range of the uncertainties of 73~123 K in Teff, 0.18~0.42 dex in log g, and 0.12~0.25 dex in [Fe/H], respectively, depending on the S/N range adopted. We conclude that these precisions are high enough to study the chemical and kinematic properties of the Galactic disk and halo stars, and we will attempt to apply this technique to Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), which plans to obtain about 8 million stellar spectra, in order to estimate stellar parameters.