• Journal of the Korean Geotechnical Society
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• v.40 no.4
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• pp.127-135
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• 2024

The purpose of this paper is to establish the relationship between compression wave velocity and porosity in unsaturated soil using a deep neural network (DNN) algorithm. Input parameters were examined using the error norm method to assess their impact on porosity. Compression wave velocity was conclusively found to have the most significant influence on porosity estimation. These parameters were derived through both field and laboratory experiments using a total of 266 numerical data points. The application of the DNN was evaluated by calculating the mean squared error loss for each iteration, which converged to nearly zero in the initial stages. The predicted porosity was analyzed by splitting the data into training and validation sets. Compared with actual data, the coefficients of determination were exceptionally high at 0.97 and 0.98, respectively. This study introduces a methodology for predicting dependent variables through error norm analysis by disregarding fewer sensitive factors and focusing on those with greater influence.

• Korean Circulation Journal
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• v.54 no.1
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• pp.30-39
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• 2024

Background and Objectives: Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters. Methods: A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images. Results: At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters; minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average. Conclusions: The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians' decision-making by real-time application in the catheterization laboratory.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.29 no.4
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• pp.247-259
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• 1993

The fishing experiment was carried out by the training ship Saebada in order to analyse the mesh selectivity for trawl cod-end, in the Southern Korea Sea and the East China Sea from June. 1991 through August, 1992. The trawl cod-end used in this experiment has the trouser type of cod-end with cover net. and the mesh selectivity was examined for the five kinds of the opening of mesh in its cod-end part. A total of 163 hauls, of which having mesh size 51.2mm ; A 89, 70.2mm ; B 54, 77.6mm ; C 55, 88.0mm ; D 52 and 111.3mm ; E 20 were used respectively. Selection curves and selection parameters were calculated by using a logistic function, S=1/(1+exp super(-(aL+b)) ). The mesh election master curves were estimated by S=1/(1+exp super(-[a(L/M)+$\beta$]) ). and the optimum mesh size were calculated with (L/M) sub(50) of master curve. In these cases 'a' and '$\alpha$' are slope, 'b' and '$\beta$' are intercept. 'L' is body length of the target species of fishes, 'M' is the mesh size, and 'S' denotes mesh selectivity. In this report, the four species of compressed form fishes were taken analized according to fish shape, and 'S' denotes mesh selectivity. In this report, the four species of compressed form fishes were taken analized according to fish shape, and the results obtained are summarized as follows: 1. Red seabream Pagrus major(Temminct et Schlegel) and yellow porgy Dentex tumifrons(Temminct et Schlegel) ; Selection rate in each mesh size of A, B, C, D and E were 99.7%, 97.5%, 91.4%, 76.7% and 57.8% respectively. Selection parameters 'a' and 'b' of mesh sizes C, D and E were 2.65 and -28.62, 4.40 and -77.73, 2.31 and -46.99, and their selection factors were 1.39, 2.10, 1.83 respectively. Selection parameters of master curve '$\alpha$' and '$\beta$' were 3.05 and -5.65 respectively, and (L/M) sub(50) was 1.85. The optimum mesh size of Red seabream was 141mm. 2. Filefish Thamnaconus modestus (Gunther) ; Selection rate in each mesh size of A, B, C, D and E were 99.6%, 98.3%, 91.2%, 80.0% and 48.6% respectively. Selection parameters 'a' and 'b' of mesh sizes C, D and E were 5.82 and -55.10, 2.92 and -36.90, 3.91 and -63.09, and their selection factors were 1.35, 1.44, 1.45 respectively. Selection parameters of master curve '$\alpha$' and '$\beta$' were 3.02 and -4.32 respectively, and (L/M) sub(50) was 1.43. The optimum mesh size was 129mm. 3. Target dory Zeus faber Valenciennes ; Selection rate in each mesh size of A, B, C, D and E were 99.7%, 100%, 83.2%, 91.6% and 65.0% respectively. Selection parameters 'a' and 'b' of mesh sizes C, D and E were 3.85 and -32.46, 4.19 and -57.38, 2.45 and -40.03, and their selection factors were 1.09, 1.56, 1.47 respectively. Selection parameters of master curve '$\alpha$' and '$\beta$' were 2.64 and -3.53 respectively, and (L/M) sub(50) was 1.34. The optimum mesh size was 127mm. 4. Butterfish Psenopsis anomala (Temminct et Schlegel) ; Selection rate in each mesh size of A, B, C, D and E were 99.2%, 34.1%, 46.5%, 14.3% and 2.4% respectively. Selection parameters 'a' and 'b' of mesh sizes B, C and D were 5.35 and -71.70, 5.07 and -69.25, 3.31 and -62.06 and their selection factors were 1.91, 1.75, 2.13 respectively. Selection parameters of master curve '$\alpha$' and '$\beta$' were 3.16 and -6.24 respectively, and (L/M) sub(50) was 1.98. The optimum mesh size was 71mm.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.552-563
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• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.6C
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• pp.883-892
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• 2004

Recently, in order to improve high speed data transmission and spectral efficiency in wireless communication systems, the study on the combination OFDM and space-time coding is active. Also, a solution to improve system capacity in multiuser systems is to use adaptive antennas. In a system using STBC, the signals transmitted from two transmit antennas are superposed at the receive antenna and the interference between two transmit antennas of a user occures. Thus it is difficult to apply the conventional beamforming techniques for single antenna to the systems using space-time coding. In this Paper, we present the MMSE beanforming technique using training sequence for STBC-OFDM systems in reverse link and evaluate the performance by using various parameters in TU and HT channels.

• Smart Media Journal
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• v.2 no.2
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• pp.33-38
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• 2013

In this paper, we investigated baseline estimation methods for remove background noise using Raman spectra from acute alcohol liver injury and acute ethanol-induced chronic liver fibrosis. Far the baseline estimation, we applied first derivative, linear programming and rolling ball method. Optimal input parameter of each method were determined by the training rate of MAP (maximum a posteriori probability) classifier. According to the experimental results, classification results baseline estimation with the rolling ball algorithm gave about 89.4%, which is very promising results for classification of acute alcohol liver injury and acute ethanol-induced chronic liver fibrosis. From these results, to determined the appropriate methods and parameters of baseline estimation impact on classification performance was confirmed.

• Computers and Concrete
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• v.12 no.5
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• pp.613-625
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• 2013

In this study, Artificial Neural Networks (ANN) analysis is used to predict the compression strength of polypropylene fibre mixed concrete. Polypropylene fibre admixture increases the compression strength of concrete to a certain extent according to mix proportion. This proportion and homogenous distribution are important parameters on compression strength. Determination of compression strength of fibre mixed concrete is significant due to the veridicality of capacity calculations. Plenty of experiments shall be completed to state the compression strength of concrete which have different fibre admixture. In each case, it is known that performing the laboratory experiments is costly and time-consuming. Therefore, ANN analysis is used to predict the 7 and 28 days of compression strength values. For this purpose, 156 test specimens are produced that have 26 different types of fibre admixture. While the results of 120 specimens are used for training process, 36 of them are separated for test process in ANN analysis to determine the validity of experimental results. Finally, it is seen that ANN analysis predicts the compression strength of concrete successfully.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.857-861
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• 2004

This paper describes preliminary results we have obtained in developing a computer vision system based on active IR illumination for real time gaze tracking for interactive graphic display. Unlike most of the existing gaze tracking techniques, which often require assuming a static head to work well and require a cumbersome calibration process for each person, our gaze tracker can perform robust and accurate gaze estimation without calibration and under rather significant head movement. This is made possible by a new gaze calibration procedure that identifies the mapping from pupil parameters to screen coordinates using the Generalized Regression Neural Networks (GRNN). With GRNN, the mapping does not have to be an analytical function and head movement is explicitly accounted for by the gaze mapping function. Furthermore, the mapping function can generalize to other individuals not used in the training. The effectiveness of our gaze tracker is demonstrated by preliminary experiments that involve gaze-contingent interactive graphic display.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.911-917
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• 2017

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.