• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.281-286
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• 2023

Diagnosing reading disorders involves complex procedures to evaluate complex cognitive processes. For an accurate diagnosis, a series of tests and evaluations by human experts are required. In this study, we propose a quantitative tool to diagnose reading disorders based on natural reading behaviors using minimal human input. The eye movements of the third- and fourth-grade students were recorded while they read a text at their own pace. Seven machine learning models were used to evaluate the gaze patterns of the words in the presented text and classify the students as normal or having a reading disorder. The accuracy of the machine learning-based diagnosis was measured using the diagnosis by human experts as the ground truth. The highest accuracy of 0.8 was achieved by the support vector machine and random forest classifiers. This result demonstrated that machine learning-based automated diagnosis could substitute for the traditional diagnosis of reading disorders and enable large-scale screening for students at an early age.

• Journal of Microbiology
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• v.39 no.1
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• pp.31-36
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• 2001

A cDNA clone encoding the ribosomal protein S20 has been isolated from the Schizosaccharomyces pombe cDNA library by colony hybridization. The insert contained in the original plasmid pYJ10 was transferred intro shuttle vector pRS316 generate plasmid pYJll. The dDNA insert of plasmid pYJll, contains 484 nucleotides and encodes a protein of 118 amino acids with a calculated mass of 13,544 daltons. The deduced amino acid sequence of S. pombe ribosomal protein S20 is very homologous with fruit fly, rat, and budding yeast counterparts. It is also homologous with Xenopus S22 ribosomal protein. S. pombe ribosomal protein S20 appears to be relatively hydruphobic except the C-terminal region. The 728 bp upstream region of the S20 gene was amplified from chromosomal DNA and transferred into the BamHI/EcoRI site of the promoterles $\beta$-galactosidase gene of the vector YEp357R, which resulted in fusion plasmid pYS20. The synthesis of $\beta$-galactosidase from the fusion plasmid appeared to be the highest in the mid-exponential phase. The S. pombe cells with the fusion plasmid grown at 35$\^{C}$ gave lower $\beta$-galactosidase activity than the cells grown at 30$\^{C}$. Computer analysis showed the consensus sequence CAGTCACA in the upstream regions of various ribosomal protein genes in S. pombe, which would be involved in the coordinated expression of small ribosomal proteins.

• Journal of Power Electronics
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• v.5 no.2
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• pp.129-141
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• 2005

A high-performance robust hybrid speed controller for a permanent-magnet synchronous motor (PMSM) drive with an on-line trained neural-network model-following controller (NNMFC) is proposed. The robust hybrid controller is a two-degrees-of-freedom (2DOF) integral plus proportional & rate feedback (I-PD) with neural-network model-following (NNMF) speed controller (2DOF I-PD NNMFC). The robust controller combines the merits of the 2DOF I-PD controller and the NNMF controller to regulate the speed of a PMSM drive. First, a systematic mathematical procedure is derived to calculate the parameters of the synchronous d-q axes PI current controllers and the 2DOF I-PD speed controller according to the required specifications for the PMSM drive system. Then, the resulting closed loop transfer function of the PMSM drive system including the current control loop is used as the reference model. In addition to the 200F I-PD controller, a neural-network model-following controller whose weights are trained on-line is designed to realize high dynamic performance in disturbance rejection and tracking characteristics. According to the model-following error between the outputs of the reference model and the PMSM drive system, the NNMFC generates an adaptive control signal which is added to the 2DOF I-PD speed controller output to attain robust model-following characteristics under different operating conditions regardless of parameter variations and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed 200F I-PD NNMF controller. The results confirm that the proposed 2DOF I-PO NNMF speed controller produces rapid, robust performance and accurate response to the reference model regardless of load disturbances or PMSM parameter variations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.4
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• pp.526-533
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• 2014

In this study, we introduce design methodology to develop a guidance for issuing heavy rainfall warning by using both RBFNNs(Radial basis function neural networks) and SVR(Support vector regression) model, and then carry out the comparative studies between two pattern classifiers. Individual classifiers are designed as architecture realized with the aid of optimization and pre-processing algorithm. Because the predictive performance of the existing heavy rainfall forecast system is commonly affected from diverse processing techniques of meteorological data, under-sampling method as the pre-processing method of input data is used, and also data discretization and feature extraction method for SVR and FCM clustering and PSO method for RBFNNs are exploited respectively. The observed data, AWS(Automatic weather wtation), supplied from KMA(korea meteorological administration), is used for training and testing of the proposed classifiers. The proposed classifiers offer the related information to issue a heavy rain warning in advance before 1 to 3 hours by using the selected meteorological data and the cumulated precipitation amount accumulated for 1 to 12 hours from AWS data. For performance evaluation of each classifier, ETS(Equitable Threat Score) method is used as standard verification method for predictive ability. Through the comparative studies of two classifiers, neuro-fuzzy method is effectively used for improved performance and to show stable predictive result of guidance to issue heavy rainfall warning.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.12
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• pp.56-65
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• 2001

This paper proposes an algorithm for the capacitance extraction of general 3-dimensional conductors in an ideal uniform dielectric that uses a high-order quadrature approximation method combined with the typical first-order collocation method to enhance the accuracy and adopts an efficient matrix-vector product algorithm for the model-order reduction to achieve efficiency. The proposed method enhances the accuracy using the quadrature method for interconnects containing corners and vias that concentrate the charge density. It also achieves the efficiency by reducing the model order using the fact that large parts of system matrices are of numerically low rank. This technique combines an SVD-based algorithm for the compression of rank-deficient matrices and Gram-Schmidt algorithm of a Krylov-subspace iterative technique for the rapid multiplication of matrices. It is shown through the performance evaluation procedure that the combination of these two techniques leads to a more efficient algorithm than Gaussian elimination or other standard iterative schemes within a given error tolerance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.105-114
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• 2011

The snake algorithm is widely adopted to track objects by extracting the active contour of the object from background. However, it fails to track the target converging to the background if there exists background whose gradient is greater than that of the pixels on the contour. Also, the contour may shrink when the target moves fast and the snake algorithm misses the boundary of the object in its searching window. To alleviate these problems, we propose an improved algorithm that can track object contour more robustly. Firstly, we propose two external energy functions, the edge energy and the contrast energy. One is designed to give more weight to the gradient on the boundary and the other to reflect the contrast difference between the object and background. Secondly, by computing the motion vector of the contour from the difference of the two consecutive frames, we can move the snake pointers of the previous frame near the region where the object boundary is probable at the current frame. Computer experiments show that the proposed method is more robust to the complicated background than the previously known methods and can track the object with fast movement.

• Journal of Korea Multimedia Society
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• v.4 no.3
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• pp.230-237
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• 2001

Adaptive filters are widely used for acoustic echo canceler, adaptive equalizer and adaptive noise canceler. Coefficients of adaptive filters are updated by NLMS algorithm. However, Coefficients are misaligned by ambient noises when they are adapted by NLMS algorithm. In this Paper, a method determined the adaptation constant by low-pass filtered instantaneous gradient vector of LMS algorithm using orthognality principles of optimal filter is proposed. At initial states, instantaneous gradient vector, that is the cross-correlation of input signals and estimation error signals, has large value because input signals are remained in estimation error signals. When an adaptive filter is conversed, the cross-correlation will be close to zero. It isn's affected by ambient noises because ambient noises are uncorrelated with input signals. Determining adaptation constant with the cross-correlation, adaptive filters can be robust to ambient noises and the convergence rate doesn't slower As results of computer simulations, it is shown that the performance of proposed algorithm is betted than that of conventional algorithms.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.6
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• pp.257-264
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• 2013

In this study, we have designed the variable amplitude threshold algorithm that can enhance the accuracy of step count using variable amplitude. This algorithm converts the x, y, z sensor values into a single energy value($E_t$) by using SVM(Signal Vector Magnitude) algorithm and can pick step count out over 99% of accuracy through the peak data detection algorithm and fixed peak threshold. To prove the results, We made the noise filtering with the fixed amplitude threshold from the amplitude of energy value that found out the detection error was increasing, and it's the key idea of the variable amplitude threshold that can be adapted on the continuous data evaluation. The experiment results shows that the variable amplitude threshold algorithm can improve the average step count accuracy up to 98.9% at 10 Hz sampling rate and 99.6% at 20Hz sampling rate.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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• pp.1557-1563
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• 2016

According to recent studies, poor sitting posture of the spine has been shown to lead to a variety of spinal disorders. For this reason, it is important to measure the sitting posture. We proposed a strategy for classification of sitting posture using machine learning. We retrieved acceleration data from single tri-axial accelerometer attached on the back of the subject's neck in 5-types of sitting posture. 6 subjects without any spinal disorder were participated in this experiment. Acceleration data were transformed to the feature vectors of principle component analysis. Support vector machine (SVM) and K-means clustering were used to classify sitting posture with the transformed feature vectors. To evaluate performance, we calculated the correct rate for each classification strategy. Although the correct rate of SVM in sitting back arch was lower than that of K-means clustering by 2.0%, SVM's correct rate was higher by 1.3%, 5.2%, 16.6%, 7.1% in a normal posture, sitting front arch, sitting cross-legged, sitting leaning right, respectively. In conclusion, the overall correction rates were 94.5% and 88.84% in SVM and K-means clustering respectively, which means that SVM have more advantage than K-means method for classification of sitting posture.

• International Journal of Vascular Biomedical Engineering
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• v.1 no.1
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• pp.13-23
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• 2003

Backgrounds: The present study in angulated coronary stenosis was to evaluate the influence of velocity and wall shear stress (WSS) on coronary atherosclerosis, the changes of hemodynamic indices following coronary stenting, as well as their effect of evolving in-stent restenosis using human in vivo hemodynamic parameters and computed simulation quantitatively and qualitatively. Methods: Initial and follow-up coronary angiographies in the patients with angulated coronary stenosis were performed (n=80). Optimal coronary stenting in angulated coronary stenosis had two models: < 50 % angle changed(model 1, n=43), > 50% angle changed group (model 2, n=37) according to percent change of vascular angle between pre- and post-intracoronary stenting. Flow-velocity wave obtained from in vivo intracoronary Doppler study data was used for in vitro numerical simulation. Spatial and temporal patterns of velocity vector and recirculation area were drawn throughout the selected segment of coronary models. WSS of pre/post-intracoronary stenting were calculated from three-dimensional computer simulation. Results: Follow-up coronary angiogram demonstrated significant difference in the percent of diameter stenosis between two groups (group 1: $40.3{\pm}30.2$ vs. group 2: $25.5{\pm}22.5%$, p<0.05). Negative WSS area on 3D simulation, which is consistent with re-circulation area of velocity vector, was noted on the inner wall of post-stenotic area before stenting. The negative WSS was disappeared after stenting. High spatial and temporal WSS before stenting fell into within physiologic WSS after stenting. This finding was prominent in Model 2 (p<0.01) Conclusions: The present study suggests that hemodynamic forces exerted by pulsatile coronary circulation termed as WSS might affect on the evolution of atherosclerosis within the angulated vascular curvature. Moreover, geometric change, such as angular difference between pre / post-intracoronary stenting might give proper information of optimal hemodynamic charateristics for vascular repair after stenting.