• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.17-33
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• 2005

This paper presents the proposed a classifier of liver cirrhotic step using MR(magnetic resonance) imaging and hierarchical neural network. The data sets for classification of each stage, which were normal, 1type, 2type and 3type, were analysis in the number of data was 231. We extracted liver region and nodule region from T1-weight MR liver image. Then objective interpretation classifier of liver cirrhotic steps. Liver cirrhosis classifier implemented using hierarchical neural network which gray-level analysis and texture feature descriptors to distinguish normal liver and 3 types of liver cirrhosis. Then proposed Neural network classifier learned through error back-propagation algorithm. A classifying result shows that recognition rate of normal is $100\%$, 1type is $82.8\%$, 2type is $87.1\%$, 3type is $84.2\%$. The recognition ratio very high, when compared between the result of obtained quantified data to that of doctors decision data and neural network classifier value. If enough data is offered and other parameter is considered this paper according to we expected that neural network as well as human experts and could be useful as clinical decision support tool for liver cirrhosis patients.

• Journal of Yeungnam Medical Science
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• v.36 no.3
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• pp.231-240
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• 2019

Background: We sought to determine the value of combining diffusion-weighted (DW) and perfusion-weighted (PW) sequences with a conventional magnetic resonance (MR) sequence to assess solid components of borderline ovarian tumors (BOTs) and stage I carcinomas. Methods: Conventional, DW, and PW sequences in the tumor imaging studies of 70 patients (BOTs, n=38; stage I carcinomas, n=32) who underwent surgery with pathologic correlation were assessed. Two independent radiologists calculated the parameters apparent diffusion coefficient (ADC), $K^{trans}$ (vessel permeability), and $V_e$ (cell density) for the solid components. The distribution on conventional MR sequence and mean, standard deviation, and 95% confidence interval of each DW and PW parameter were calculated. The inter-observer agreement among the two radiologists was assessed. Area under the receiver operating characteristic curve (AUC) and multivariate logistic regression were performed to compare the effectiveness of DW and PW sequences for average values and to characterize the diagnostic performance of combined DW and PW sequences. Results: There were excellent agreements for DW and PW parameters between radiologists. The distributions of ADC, $K^{trans}$, and $V_e$ values were significantly different between BOTs and stage I carcinomas, yielding AUCs of 0.58 and 0.68, 0.78 and 0.82, and 0.70 and 0.72, respectively, with ADC yielding the lowest diagnostic performance. The AUCs of the DW, PW, and combined PW and DW sequences were $0.71{\pm}0.05$, $0.80{\pm}0.05$, and $0.85{\pm}0.05$, respectively. Conclusion: Combining PW and DW sequences to a conventional sequence potentially improves the diagnostic accuracy in the differentiation of BOTs and stage I carcinomas.

• Structural Engineering and Mechanics
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• v.85 no.5
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• pp.679-691
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• 2023

Many codes allow designers to use the bending moment diagram computed by elastic analysis and modify it by a certain amount of moment redistribution (MR) to account for plastic behaviour of continuous beams. However, several researchers indicated that the MR at the ultimate limit state (𝛽_u) for some beams deviate significantly from the specified values of various codes. This paper examines the applicability of the provisions on 𝛽_u in ACI 318-19 and Eurocode 2 through numerical investigations and comprehensively explores the influencing factors. The results show that some parameters not considered in those codes influence 𝛽_u to a certain extent, where the ratio of tensile reinforcement ratio at intermediate support to tensile reinforcement ratio at midspan (𝜌_s1/𝜌_s2) and load type are crucial parameters to consider. The specific combination of these two parameters may make the codes overestimate or significantly underestimate the 𝛽_u. On the other hand, the yield state of both critical sections is found to have an important influence on the influence degree of each parameter on 𝛽_u. The yield conditions are investigated, and an empirical judgment equation is proposed. In addition, the influence laws of the critical parameters on 𝛽_u have been further proved by theoretical derivation. Finally, due to 𝜀_t is found to have a better linear correlation with 𝛽_u than x_u/d, equations as a function of 𝜀_t for predicting the 𝛽_u of continuous beams under the two loads are proposed, respectively.

• Investigative Magnetic Resonance Imaging
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• v.1 no.1
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• pp.103-107
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• 1997

Purpose: To investigate the localization and functional lateralization of the supplementary motor area (SMA) in motor activation tests in comparison to that of the primary motor area. Materials and Methods: Seven healthy volunteers obtained echoplanar imaging blood oxygen level dependent technique. This study was carried on 1.5T Siemens Magnetom Vision system with the standard head coil. Parameters of EPI were followed as; TR/TE : 1.0/66.0msec, flip angle: $90^{\circ}$, field of view: $22cm{\times}22cm,{\;}matrix:{\;}128{\times}128$, slice number/slice thickness/gap: 1O/4mm/0.8mm with fat suppression technique. Motor task as finger opposition in each hand consisted of 3 sets of alternative rest and activation periods. Postprocessing were done on Stimulate 5.0 by using cross-correlation statistics. To compare the functional lateralization of the SMA in the right and left hand tests, each examination was evaluated for the percent change of signal intensity and the number of activated voxels both in the SMA and in the pri¬mary motor area. Hemispheric asymmetry was defined as difference of summation of the activted voxels between each hemisphere. Results: Percent change of signal intensity in the SMA (2.49 -3.06%) is lower than that of primary motor area(4.4 -7.23%). Percent change of signal intensity including activated voxels were observed almost equally in the right and left SMA. As for summation of activated voxels, primary motor area had significant difference between each hemisphere but not did the SMA. Conclusion: Preferred contralateral dominant hemisphere and hemispheric asymmetry were detected in the primary motor area but not in the SMA.

• Journal of radiological science and technology
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• v.26 no.1
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• pp.71-82
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• 2003

In this paper, I proposed a classifier of liver cirrhotic step using T1-weighted MRI(magnetic resonance imaging) and hierarchical neural network. The data sets for classification of each stage, which were normal, 1type, 2type and 3type, were obtained in Pusan National University Hospital from June 2001 to december 2001. And the number of data was 46. We extracted liver region and nodule region from T1-weighted MR liver image. Then objective interpretation classifier of liver cirrhotic steps in T1-weighted MR liver images. Liver cirrhosis classifier implemented using hierarchical neural network which gray-level analysis and texture feature descriptors to distinguish normal liver and 3 types of liver cirrhosis. Then proposed Neural network classifier teamed through error back-propagation algorithm. A classifying result shows that recognition rate of normal is 100%, 1type is 82.3%, 2type is 86.7%, 3type is 83.7%. The recognition ratio very high, when compared between the result of obtained quantified data to that of doctors decision data and neural network classifier value. If enough data is offered and other parameter is considered, this paper according to we expected that neural network as well as human experts and could be useful as clinical decision support tool for liver cirrhosis patients.

• Smart Structures and Systems
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• v.19 no.1
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• pp.21-31
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• 2017

To control the stochastic vibration of a vibration-sensitive instrument supported on a beam, the beam is designed as a sandwich structure with magneto-rheological visco-elastomer (MRVE) core. The MRVE has dynamic properties such as stiffness and damping adjustable by applied magnetic fields. To achieve better vibration control effectiveness, the optimal bounded parametric control for the MRVE sandwich beam with supported mass under stochastic and deterministic support motion excitations is proposed, and the stochastic and shock vibration suppression capability of the optimally controlled beam with multi-mode coupling is studied. The dynamic behavior of MRVE core is described by the visco-elastic Kelvin-Voigt model with a controllable parameter dependent on applied magnetic fields, and the parameter is considered as an active bounded control. The partial differential equations for horizontal and vertical coupling motions of the sandwich beam are obtained and converted into the multi-mode coupling vibration equations with the bounded nonlinear parametric control according to the Galerkin method. The vibration equations and corresponding performance index construct the optimal bounded parametric control problem. Then the dynamical programming equation for the control problem is derived based on the dynamical programming principle. The optimal bounded parametric control law is obtained by solving the programming equation with the bounded control constraint. The controlled vibration responses of the MRVE sandwich beam under stochastic and shock excitations are obtained by substituting the optimal bounded control into the vibration equations and solving them. The further remarkable vibration suppression capability of the optimal bounded control compared with the passive control and the influence of the control parameters on the stochastic vibration suppression effectiveness are illustrated with numerical results. The proposed optimal bounded parametric control strategy is applicable to smart visco-elastic composite structures under deterministic and stochastic excitations for improving vibration control effectiveness.

• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.295-303
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• 2021

Idiopathic normal-pressure hydrocephalus (INPH) is considered a potentially treatable neurological disorder by shunt surgery and characterized by a triad of symptoms including gait disturbance, cognitive impairment and urinary dysfunction. Although disorders of white matter are generally viewed as the principal pathological features of INPH, analysis of cortical features are important since the destruction of neural tracts could be associated with cortical structural changing. The aim of the study was to determine whether there was any relationship between gait parameter and structural features of cerebral cortex in INPH patients. Gait parameters were measured as follows: step width, toe in/out angle, coefficient of variation (CV) value of stride length, CV value of stride time. After obtaining individual brain MRI of patients with INPH and hemispheric cortical surfaces were automatically extracted from each MR volume, which reconstructed the inner and outer cortical surface. Then, cortical thickness, surface area, and volume were calculated from the cortical surface. As a result, step width was positively correlated with bilateral postcentral gyrus and left precentral gyrus, and toe in/out was positively correlated with left posterior parietal cortex and left insula. Also, the CV value of stride length showed positive correlation in the right superior frontal sulcus, left insula, and the CV value of stride time showed positive correlation in the right superior frontal sulcus. Unique parameter of cerebral cortical changes, as measured using MRI, might underline impairments in distinct gait parameters in patients with INPH.

• Journal of the Korean Society of Visualization
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• v.12 no.1
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• pp.35-42
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• 2014

Magnetic resonance velocimetry (MRV) is a versatile flow visualization technique using magnetic resonance imaging machine developed for the medical purpose. Recently, MRV is often utilized to analyze engineering flows due to its superior features of MRV such as capabilities of measuring flows with complicated, opaque flow geometry unlike optical techniques, 3-dimensional volumetric velocity vectors within a few hours, and etc. The purpose of this study was to validate the MRV data and evaluate the accuracy of the mean velocity profiles that we acquired for a turbulent flow in a circular pipe using a MR machine installed in Korea Basic Science Institute, Ochang, Korea. In addition, we briefly describe a procedure of parameter optimization for the operation of MRV. The results indicate that the MRV measurements provided well resolved mean velocity fields with a quite reasonable accuracy according to the inner and outer layer scaling laws of the turbulent pipe flows.

• Proceedings of the KSMRM Conference
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• 2001.11a
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• pp.112-112
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• 2001

Purpose： We developed the software for diffusion tensor imaging and evaluated its feasibility in norm brains. Method： Five normal volunteers, aged from 25 to 29 years, were examined on a 1.5 T MR system. the diffusion tensor pulse sequence used a SE-EPI with 6 diffusion gradie directions of (1, 1, 0), (-1, 1,0), (1, 0, 1), (-1, 0, 1), (0, 1, 1), (0, 1, -1) and also with no diffusion gradient. A b-factor of 500 sec/mm2 was used. Measurement parameter were as follows； TR/TE=10000 ms/99 ms, FOV=240 mm, matrix=128$\times$128, slice thickness/gap=6 mm/0 mm, bandwidth=91 kHz and the number of total slices=20. Four repeated axial diffusion images were averaged for diffusion tensor imaging. A total scan 11 of 4 min 30 sec was used. Six full diffusion tensor components of Dxx, Dyy, Dzz, Dxy, Dxz and Dyz were obtained using two-point linear regression model from 7 diffusion-weight images at each pixel and fractional anisotropy and lattice index images was estimated fr their eigenvectors and eigenvalues. Our program was written on a platform of IDL. W evaluated the qualities of fractional anisotropy and lattice index images of normal brains a knew whether our software for diffusion tensor imaging may be feasible.

• Journal of Aerospace System Engineering
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• v.14 no.2
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• pp.20-27
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• 2020

Several uncertainties in the landing environment of an aircraft are not considered, such as the falling speed, ambient temperature, and sensor noise. These uncertainties negatively affect the performance of the controller applied to a landing gear. The sliding mode control (SMC) method, which maintains the optimal performance of a controller under uncertainties, is used in this study. The landing gear is equipped with a magnetorheological damper that changes the yield shear stress according to the applied magnetic field. The applied controller employs a hybrid control combining Skyhook control and force control. The SMC maintains the optimal performance of the hybrid control by minimizing the tracking error of the damper force, even in various landing environments where parameter uncertainties are applied. The effect of SMC is verified through co-simulation results from Simscape and Simulink.