• 제목/요약/키워드: Iterative learning technique

검색결과 41건 처리시간 0.022초

전산화단층촬영조영술에서 화질 최적화를 위한 딥러닝 기반 및 하이브리드 반복 재구성의 특성분석 (Characterization of Deep Learning-Based and Hybrid Iterative Reconstruction for Image Quality Optimization at Computer Tomography Angiography)

  • 전필현;이창래
    • 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.

Improvement of trajectory tracking control performance by using ILC

  • Le, Dang-Khanh;Nam, Taek-Kun
    • Journal of Advanced Marine Engineering and Technology
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    • 제38권10호
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    • pp.1281-1286
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    • 2014
  • This paper presents an iterative learning control (ILC) approach for tracking problems with specified data points that are desired points at certain time instants. To design ILC systems for such problems, unlike traditional ILC approaches, an algorithm which updates not only the control signal but also the reference trajectory at each trial will be developed. The relationship between the reference trajectory and ILC control in tracking problems where there are specified data points through which the system should pass is investigated as the rate of convergence. In traditional ILC, the desired data is stored in a tracking profile file. Due to the huge size of the data file containing the target points, it is important to reduce the computational cost. Finally, simulation results of the presented technique are mentioned and compared to other related works to confirm the effectiveness of proposed scheme.

반복학습에 의한 MIMO Nonminimum Phase 자율주행 System의 Feedforward 입력신호 생성에 관한 연구 (Feedforward Input Signal Generation for MIMO Nonminimum Phase Autonomous System Using Iterative Learning Method)

  • 김경수
    • 한국군사과학기술학회지
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    • 제21권2호
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    • pp.204-210
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    • 2018
  • As the 4th industrial revolution and artificial intelligence technology develop, it is expected that there will be a revolutionary changes in the security robot. However, artificial intelligence system requires enormous hardwares for tremendous computing loads, and there are many challenges that need to be addressed more technologically. This paper introduces precise tracking control technique of autonomous system that need to move repetitive paths for security purpose. The input feedforward signal is generated by using the inverse based iterative learning control theory for the 2 input 2 output nonminimum-phase system which was difficult to overcome by the conventional feedback control system. The simulation results of the input signal generation and precision tracking of given path corresponding to the repetition rate of extreme, such as bandwidth of the system, shows the efficacy of suggested techniques and possibility to be used in military security purposes.

COLLISION-FREE TRAJECTRY PLANNING FOR DUAL ROBOT ARMS USING ITERATIVE LEARNING CONCEPT

  • Suh, Il-Hong;Chong, Nak-Young;Choi, Donghun;Shin, Kang-G.
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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    • pp.627-634
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    • 1989
  • A collision-free trajectory planning algorithm using the iterative learning concept is proposed for dual robot arms in a 3-D workspace to accurately follow their specified paths with constant velocities. Specifically, a collision-free trajectory minimizing the trajectory error is obtained first by employing the linear programming technique. Then the total operating time is iteratively adjusted based on the maximum trajectory error of the previous iteration so that the collision-free trajectory has no deviation from the specified path and also the operating time is near-minimal.

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신경회로망을 이용한 코드북의 순차적 갱신 알고리듬 (An Algorithm to Update a Codebook Using a Neural Net)

  • 정해묵;이주희;이충웅
    • 대한전자공학회논문지
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    • 제26권11호
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    • pp.1857-1866
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    • 1989
  • In this paper, an algorithm to update a codebook using a neural network in consecutive images, is proposed. With the Kohonen's self-organizing feature map, we adopt the iterative technique to update a centroid of each cluster instead of the unsupervised learning technique. Because the performance of this neural model is comparable to that of the LBG algorithm, it is possible to update the codebooks of consecutive frames sequentially in TV and to realize the hardwadre on the real-time implementation basis.

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Model-based Predictive Control Approach to Continuous Process based on Iterative Learning Concept

  • Chin, In-Sik;Cho, Moon-Ki;Lee, Jay-H;Lee, Kwang-Soon
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2001년도 ICCAS
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    • pp.41.1-41
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    • 2001
  • Since the advanced control technique such as model predictive control has been introduced to industrial plant, there have been many progresses in the process control. As a way to improve the control performance, the on-line process optimizer was integrated with the advance controller. In this study, a control technique which improves the control. As the number of changes by the optimizer is increased, the control performance of the proposed algorithm is improved. Its control performance is shown via an numerical example.

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In-process Truing of Metal-bonded Diamond Wheels for Electrolytic In-process Dressing (ELID) Grinding

  • Saleh, Tanveer;Biswas, Indraneel;Lim, Han-Seok;Rahman, Mustafizur
    • International Journal of Precision Engineering and Manufacturing
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    • 제9권3호
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    • pp.3-6
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    • 2008
  • Electrolytic in-process dressing (ELID) grinding is a new technique for achieving a nanoscale surface finish on hard and brittle materials such as optical glass and ceramics. This process applies an electrochemical dressing on the metal-bonded diamond wheels to ensure constant protrusion of sharp cutting grits throughout the grinding cycle. In conventional ELID grinding, a constant source of pulsed DC power is supplied to the ELID cell, but a feedback mechanism is necessary to control the dressing power and obtain better performance. In this study, we propose a new closed-loop wheel dressing technique for grinding wheel truing that addresses the efficient correction of eccentric wheel rotation and the nonuniformity in the grinding wheel profile. The technique relies on an iterative control algorithm for the ELID power supply. An inductive sensor is used to measure the wheel profile based on the gap between the sensor head and wheel edge, and this is used as the feedback signal to control the pulse width of the power supply. We discuss the detailed mathematical design of the control algorithm and provide simulation results that were confirmed experimentally.

Advanced controller design for AUV based on adaptive dynamic programming

  • Chen, Tim;Khurram, Safiullahand;Zoungrana, Joelli;Pandey, Lallit;Chen, J.C.Y.
    • Advances in Computational Design
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    • 제5권3호
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    • pp.233-260
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    • 2020
  • The main purpose to introduce model based controller in proposed control technique is to provide better and fast learning of the floating dynamics by means of fuzzy logic controller and also cancelling effect of nonlinear terms of the system. An iterative adaptive dynamic programming algorithm is proposed to deal with the optimal trajectory-tracking control problems for autonomous underwater vehicle (AUV). The optimal tracking control problem is converted into an optimal regulation problem by system transformation. Then the optimal regulation problem is solved by the policy iteration adaptive dynamic programming algorithm. Finally, simulation example is given to show the performance of the iterative adaptive dynamic programming algorithm.

Kernel Adatron Algorithm for Supprot Vector Regression

  • Kyungha Seok;Changha Hwang
    • Communications for Statistical Applications and Methods
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    • 제6권3호
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    • pp.843-848
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    • 1999
  • Support vector machine(SVM) is a new and very promising classification and regression technique developed by Bapnik and his group at AT&T Bell laboratories. However it has failed to establish itself as common machine learning tool. This is partly due to the fact that SVM is not easy to implement and its standard implementation requires the optimization package for quadratic programming. In this paper we present simple iterative Kernl Adatron algorithm for nonparametric regression which is easy to implement and guaranteed to converge to the optimal solution and compare it with neural networks and projection pursuit regression.

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Fast Real-Time Cardiac MRI: a Review of Current Techniques and Future Directions

  • Wang, Xiaoqing;Uecker, Martin;Feng, Li
    • Investigative Magnetic Resonance Imaging
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    • 제25권4호
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    • pp.252-265
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    • 2021
  • Cardiac magnetic resonance imaging (MRI) serves as a clinical gold-standard non-invasive imaging technique for the assessment of global and regional cardiac function. Conventional cardiac MRI is limited by the long acquisition time, the need for ECG gating and/or long breathhold, and insufficient spatiotemporal resolution. Real-time cardiac cine MRI refers to high spatiotemporal cardiac imaging using data acquired continuously without synchronization or binning, and therefore of potential interest in overcoming the limitations of conventional cardiac MRI. Novel acquisition and reconstruction techniques must be employed to facilitate real-time cardiac MRI. The goal of this study is to discuss methods that have been developed for real-time cardiac MRI. In particular, we classified existing techniques into two categories based on the use of non-iterative and iterative reconstruction. In addition, we present several research trends in this direction, including deep learning-based image reconstruction and other advanced real-time cardiac MRI strategies that reconstruct images acquired from real-time free-breathing techniques.