• International journal of advanced smart convergence
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• 제7권2호
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• pp.33-37
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• 2018

In this study, we propose an iterative clustering algorithm that automatically clusters a set of voice signal data without a label into an optimal number of clusters and generates hmm model for each cluster. In the clustering process, the likelihood calculations of the clusters are performed using iterative hmm learning and testing while varying the number of clusters for given data, and the maximum likelihood estimation method is used to determine the optimal number of clusters. We tested the effectiveness of this clustering algorithm on a small-vocabulary digit clustering task by mapping the unsupervised decoded output of the optimal cluster to the ground-truth transcription, we found out that they were highly correlated.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국내학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.326-331
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• 1988

In this paper, an iterative leaning control algorithm for the linear discrete system is proposed. Based upon the parameter estimation method, the learning for good tracking control is acqured through a sequence of repetitive operations. A series of simulation are performed to show the validity of this algorithm.

• 한국방사선학회논문지
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• 제17권1호
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• pp.1-9
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• 2023

전산화단층촬영조영술(computer tomography angiography, CTA)의 최적 화질을 위한 서로 다른 요오드 농도와 스캔 매개변수를 적용하여 필터 보정 역투영 (filtered back projection, FBP), 혼합형 반복재구성 (hybrid-iterative reconstruction, hybrid-IR) 및 딥러닝 재구성 (deep learning reconstruction, DLR)의 화질적 특성을 정량적으로 평가하였다. 320행 검출기 CT 스캐너에서 지름 19 cm의 원통형 물 팬텀 가장자리에 있는 다양한 요오드 농도 (1.2, 2.9, 4.9, 6.9, 10.4, 14.3, 18.4 및 25.9 mg/mL)의 팬텀을 스캔하였다. 각각의 재구성 기술을 사용하여 획득한 데이터는 노이즈 (noise), 변동 계수 (coefficient of variation, COV) 및 평균 제곱근 오차 (root mean square error, RMSE)을 통해 영상을 분석하였다. 요오드의 농도가 증가할수록 CT number 값은 증가하였지만 노이즈 변화는 특별한 특성을 보이지 않았다. 다양한 관전류 및 관전압에서 FBP, adaptive iterative dose reduction (AIDR) 3D 및 advanced intelligent clear-IQ engine (AiCE)에 대해 요오드 농도를 증가할수록 COV는 감소하였고 요오드 농도가 낮을 때는 재구성 기술 간의 COV 차이가 다소 발생하였지만, 요오드 농도가 높아짐에 따라 그 차이는 미약한 결과를 보였다. 또한, AiCE에서는 요오드 농도가 높아질수록 RMSE는 감소하지만 특정한 농도 (4.9 mg/mL) 이후에는 RMSE가 오히려 증가 되는 특성을 보여주었다. 따라서 최적의 CTA 영상 획득을 위해 재구성 기술에 따른 요오드 농도의 변화 및 다양한 관전류 및 관전압의 스캔 매개변수의 특성을 고려하여 환자 스캔을 해야 할 것이다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 하계종합학술대회 논문집
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• pp.112-115
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• 1989

This paper proposes a learning control algorithm for trajectory tracking of large scale system. The controller using only localized informations is composed of stabilizing controller and iterative learning controller. Stabilization and convergence of each subsystem is assured under some conditions which are inequalities of inter-connection terms and learning controller gain.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.1277-1283
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• 1990

The iterative learning control synthesized in the frequency domain has been utilized for temperature control of a batch reactor. For this purpose, a feedback-assisted generalized learning control scheme was constructed first, and the convergence and robustness analyses were conducted in the frequency domain. The feedback-assisted learning operation was then implemented in a bench scale batch reactor where reaction heat is simulated using an electric heater. As a result, progressive reduction of temperature control error could be obviously observed as batch operation is repeated.

• 전자공학회논문지S
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• 제34S권7호
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• pp.56-65
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• 1997

In this paper, we propose a reference input odification-type iterative learning control law for a class of discrete-time nonlinear systems and prove the convergence of the output error. We can get the high-precision in case of the trajectroy control when the proposed control law is properly combined with a feedback controller, and we can easily implement the learning control law compared to the control input modification-type learning control law. To show the validity and the convergence perfodrmance of the proposed control law, we perform experimentations on the trajectroy control and rejection of periodic disturbance for a 2-axis SCARA-type direct drive robot.

• Korean Journal of Radiology
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• 제21권10호
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• pp.1165-1177
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• 2020

Objective: To assess the feasibility of applying a deep learning-based denoising technique to coronary CT angiography (CCTA) along with iterative reconstruction for additional noise reduction. Materials and Methods: We retrospectively enrolled 82 consecutive patients (male:female = 60:22; mean age, 67.0 ± 10.8 years) who had undergone both CCTA and invasive coronary artery angiography from March 2017 to June 2018. All included patients underwent CCTA with iterative reconstruction (ADMIRE level 3, Siemens Healthineers). We developed a deep learning based denoising technique (ClariCT.AI, ClariPI), which was based on a modified U-net type convolutional neural net model designed to predict the possible occurrence of low-dose noise in the originals. Denoised images were obtained by subtracting the predicted noise from the originals. Image noise, CT attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were objectively calculated. The edge rise distance (ERD) was measured as an indicator of image sharpness. Two blinded readers subjectively graded the image quality using a 5-point scale. Diagnostic performance of the CCTA was evaluated based on the presence or absence of significant stenosis (≥ 50% lumen reduction). Results: Objective image qualities (original vs. denoised: image noise, 67.22 ± 25.74 vs. 52.64 ± 27.40; SNR [left main], 21.91 ± 6.38 vs. 30.35 ± 10.46; CNR [left main], 23.24 ± 6.52 vs. 31.93 ± 10.72; all p < 0.001) and subjective image quality (2.45 ± 0.62 vs. 3.65 ± 0.60, p < 0.001) improved significantly in the denoised images. The average ERDs of the denoised images were significantly smaller than those of originals (0.98 ± 0.08 vs. 0.09 ± 0.08, p < 0.001). With regard to diagnostic accuracy, no significant differences were observed among paired comparisons. Conclusion: Application of the deep learning technique along with iterative reconstruction can enhance the noise reduction performance with a significant improvement in objective and subjective image qualities of CCTA images.

• 제어로봇시스템학회논문지
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• 제10권5호
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• pp.395-401
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• 2004

We present an AILC(Adaptive Iterative Learning Control) scheme and a sufficient condition for system parameter identification for uncertain robotic systems that perform the same tasks repetitively. It is guaranteed that the joint velocity and position asymptotically converge to the reference joint velocity and position, respectively. In addition, it is proved that a sufficient condition for parameter identification is the PE(Persistent Excitation) condition on the regressor matrix evaluated at the reference trajectory during the operation period. Since the regressor matrix on the reference trajectory can be easily computed prior to the real robot operation, the proposed algorithm provides a useful method to verify whether the parameter error converges to zero or not.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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• pp.797-799
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• 2000

Most neural network learning schemes are derived from learning systems which are generally iterative in nature. But, when the given input-output training vector pairs satisfy a PLI condition, the training and the application of a hard-limited neural network can be achieved non-iteratively with very short training time and very robust recognition when it is applied to recognize any untrained patterns. In this paper, a method of expanding the dimension of training pattern data is suggested. The proposed method demonstrates better performance and robustness.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1989년도 추계학술대회 논문집 학회본부
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• pp.367-371
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• 1989

As a menas of designing a robust servo system for electrical motor drive system, an iterative learning control method is proposed by employing the structure of the model algorithmic control. A sufficient condition for the convergency is shown, and via simulation for permanent magnet synchronous motor drive system, it is demonstrated 1hat the method yields a 'good performance even in the presence of the external load distrurbances.