• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.300-312
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• 2021

Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learning-based methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learning-based framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.

• Journal of The Korean Radiological Technologist Association
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• v.28 no.1
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• pp.367-367
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• 2002

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.4
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• pp.683-690
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• 2012

In this work, the semi-active control of a large-sized bus suspension system with an MR damper was studied. An MR damper model that can aptly describe the hysteretic characteristics of an MR damper was adopted. Parameter values of the MR damper model were suitably modified by considering the maximum damping force of a passive damper used in the suspension system of a real large-sized bus. In addition, a fuzzy logic controller was developed for semi-active control of a suspension system with an MR damper. The vertical acceleration at the attachment point of the MR damper and the relative velocity between sprung and unsprung masses were used as input variables, while voltage was used as the output variable. Straight-ahead driving simulations were performed on a road with a random road profile and on a flat road with a bump. In straight-ahead driving simulations, the vertical acceleration and pitch angle were measured to compare the riding performance of a suspension system with a passive damper with that of a suspension with an MR damper. In addition, a single lane change simulation was performed. In the simulation, the lateral acceleration and roll angle were measured in order to compare the handling performance of a suspension system using a passive damper with that of a suspension system using an MR damper.

• 유체기계공업학회:학술대회논문집
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• 1998.02a
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• pp.220-227
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• 1998

This paper evaluate the performance of a Magneto-Rheological (MR) fluid mount. The mount incorporates MR fluid in a conventional fluid mount to open and closed the inertia track between the fluid chambers of the mount. It is shown that such switching of the inertia track improves the mount's isolation effect, by eliminating the large transmissibility peak that commonly exists at frequencies higher than the notch frequency for conventional fluid mounts. The switching frequencies of the MR mount is evaluated, based on the parameters of the mount. A simple control scheme for switching the mount between the open and closed states is proposed, and the performance of the controlled mount is compared with conventional mounts. A sensitivity analysis is conducted to evaluate the effect of parameter errors in estimating the switching frequencies and mount performance. The results show that the switching frequencies can be accurately determined from mount parameters that are easily measured or estimated.

• Smart Structures and Systems
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• v.23 no.6
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• pp.553-564
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• 2019

Modeling of magnetorheological (MR) dampers for cable vibration control to facilitate the design of even more effective and economical systems is still a challenging task. In this study, a parameter-adaptive three-element model is first established for a full-scale MR damper based on laboratory tests. The parameters of the model are represented by a set of empirical formulae in terms of displacement amplitude, voltage input, and excitation frequency. The model is then incorporated into the governing equation of cable-damper system for investigation of open-loop vibration control of stay cables in a cable-stayed bridge. The concept of optimal voltage/current input achieving the maximum damping for the system is put forward and verified. Multi-mode suboptimal and Single-mode optimal open-loop control method is then developed. Important conclusions are drawn on application issues and unique characteristics of open-loop cable vibration control using MR dampers.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.8
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• pp.1224-1230
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• 2015

This paper is concerned with applicability of intelligent PID controller which is proposed by Fliss and Join recently. First, we analyze the stability regions of intelligent PID control systems when parameter α is varying, and propose a new method to determine the suitable range of α by using the roots locus. Second, the simulation study of magneto-rheological (MR) damper to the structural vibrations due to earthquakes is presented to verify the effectiveness of the intelligent PID control method.

• The Transactions of the Korea Information Processing Society
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• v.7 no.1
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• pp.286-295
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• 2000

We present a novel statistically adaptive method using the Minimum Description Length(MDL) principle for unsupervised segmentation of magnetic resonance(MR) images. In the method, Markov random filed(MRF) modeling of tissue region accounts for random noise. Intensity measurements on the local region defined by a window are modeled by a finite Gaussian mixture, which accounts for image inhomogeneities. The segmentation algorithm is based on an iterative conditional modes(ICM) algorithm, approximately finds maximum ${\alpha}$ posteriori(MAP) estimation, and estimates model parameters on the local region. The size of the window for parameter estimation and segmentation is estimated from the image using the MDL principle. In the experiments, the technique well reflected image characteristic of the local region and showed better results than conventional methods in segmentation of MR images with inhomogeneities, especially.

• Investigative Magnetic Resonance Imaging
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• v.23 no.2
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• pp.81-99
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• 2019

Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.

• Investigative Magnetic Resonance Imaging
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• v.25 no.2
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• pp.76-80
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• 2021

Post-concussion syndrome (PCS) following mild traumatic brain injury (mTBI) is a major factor that contributes to the increased socioeconomic burden caused by TBI. Myelin loss has been implicated in the development of PCS following mTBI. Diffusion tensor imaging (DTI), a traditional imaging modality for the evaluation of axonal and myelin integrity in mTBI, has intrinsic limitations, including its lack of specificity and its time-consuming and labor-intensive post-processing analysis. More recently, various fast MR techniques based on multicomponent relaxometry (MCR), including QRAPMASTER, mcDESPOT, and MDME sequences, have been developed. These MCR-based sequences can provide myelin water fraction/myelin volume fraction, a quantitative parameter more specific to myelin, which might serve as a surrogate marker of myelin volume, in a clinically feasible time. In this review, we summarize the clinical application of the MCR-based fast MR techniques in mTBI patients.

• Proceedings of the Korean Society of Applied Pharmacology
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• 1994.04a
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• pp.334-334
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• 1994

Omeprazole의 약동학적 Parameter는 tmax를 제외하고 두 군간에 유의한 (p<0.001) 차이론 보였다 : AUC의 평균치는 5-mephenytoin hydroxylation poor metabol izer에서 extensive metabol izer에 비해 약 6-7배 컸다. Omeprazole sulfone의 parameter는 omeprazole에서 관찰된 두 군간의 차이와 유사하였다. 그러나 5-hydroxyomeprazole의 경우에는 Cmax, AUC 등이 extensive metabolizer에서 더컸다. Omeprazole의 청소율은 S-mephenytoin hydroxylation 대사능(MR)과 유의한 상잔관계(rs=0.79, p<0.01)를 보였으며, omeprazole과 그 대사물(5-hydroxyomeprazole 및 omeprazole sulfone)의 반감기 또한 S-mephenytoin hydroxylation 대사능과 유의한 상관성을 보였다.