• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.53-66
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• 2021

Velocity model building is an essential procedure in seismic data processing. Conventional techniques, such as traveltime tomography or velocity analysis take longer computational time to predict a single velocity model and the quality of the inversion results is highly dependent on human expertise. Full-waveform inversions also depend on an accurate initial model. Recently, deep neural network techniques are gaining widespread acceptance due to an increase in their integration to solving complex and nonlinear problems. This study investigated cases of seismic velocity model building using deep neural network techniques by classifying items according to the neural networks used in each study. We also included cases of generating training synthetic velocity models. Deep neural networks automatically optimize model parameters by training neural networks from large amounts of data. Thus, less human interaction is involved in the quality of the inversion results compared to that of conventional techniques and the computational cost of predicting a single velocity model after training is negligible. Additionally, unlike full-waveform inversions, the initial velocity model is not required. Several studies have demonstrated that deep neural network techniques achieve outstanding performance not only in computational cost but also in inversion results. Based on the research results, we analyzed and discussed the characteristics of deep neural network techniques for building velocity models.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.313-318
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• 2015

A traditional speech enhancement methods distort the sound spectrum generated according to estimation of the remaining noise, or invalid noise is a problem of lowering the speech recognition performance. In this paper, we propose a speech detection method that convergence the sound energy distribution process and sound energy parameters. The proposed method was used to receive properties reduce the influence of noise to maximize voice energy. In addition, the smaller value from the feature parameters of the speech signal The log energy features of the interval having a more of the log energy value relative to the region having a large energy similar to the log energy feature of the size of the voice signal containing the noise which reducing the mismatch of the training and the recognition environment recognition experiments Results confirmed that the improved recognition performance are checked compared to the conventional method. Car noise environment of Pause Hit Rate is in the 0dB and 5dB lower SNR region showed an accuracy of 97.1% and 97.3% in the high SNR region 10dB and 15dB 98.3%, showed an accuracy of 98.6%.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.359-365
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• 2001

In this paper, we propose the design methodology of target tracking system using fuzzy basis function expansion(FBFE) based on virus evolutionary genetic algorithm (VEGA). In general, the objective of target tracking is to estimate the future trajectory of the target based on the past position of the target obtained from the sensor. In the conventional and mathematical nonlinear filtering method such as extended Kalman filter(EKF), the performance of the system may be deteriorated in highly nonlinear situation. To resolve these problems of nonlinear filtering technique, by appling artificial intelligent technique to the tracking control of moving targets, we combine the advantages of both traditional and intelligent control technique. In the proposed method, after composing training datum from the parameters of extended Kalman filter, by combining FDFE, which has the strong ability for the approximation, with VEGA, which prevent GA from converging prematurely in the case of lack of genetic diversity of population, and by idenLifying the parameters and rule numbers of fuzzy basis function simultaneously, we can reduce the tracking error of EKF. Finally, the proposed method is applied to three dimensional tracking problem, and the simulation results shows that the tracking performance is improved by the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.239-247
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• 2018

Adaptive Beamforming (ABF) algorithm, which is a typical jammer suppression algorithm, guarantees the performance on the assumption that the jamming characteristics of the TDS (Training Data Sample) are stationary, which are obtained immediately before and after transmitting the pulse signal. Therefore, effective jammer suppression can not be expected when the jamming characteristics are non-stationary. In this paper, we propose a new jammer suppression algorithm, of which power spectrum fluctuates fast. In this case, we assume that the location of the jammer station is fixed during the processing time. By applying the MPM (Matrix Pencil Method) to the jamming signal in TDS, we can estimate jammer parameters such as power and incident angle, of which the power will vary fast in time or range bins after TDS. Though we assume that the jammer station is fixed, the estimated jammer's incident angle has an error due to the noise, which degrades the performance of the jammer suppression as the jammer power increases fast. Therefore, the jammer's incident angle should be re-estimated at each range bin after TDS. By using the re-estimated jammer's incident angle, we can construct new covariance matrix under the non-stationary jamming environment. Then, the optimum weight for the jammer suppression is obtained by inversing matrix estimation method based on the matrix projection with the estimated jammer parameters as variables. To verify the performance of the proposed algorithm, the SINR (signal-to-interference plus noise ratio) loss of the proposed algorithm is compared with that of the conventional ABF algorithm.

• Journal of Biomedical Engineering Research
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• v.26 no.5
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• pp.343-349
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• 2005

Continuously motivating people to exercise regularly is more important than finding a way out of barriers such as lack of time, cost of equipment, lack of nearby facilities, and poor weather. Our proposed system presents practicable methods of motivation through a diverse exercise aid system. The Health Improvement and Management System (all-in-one system which saves space and maintenance costs) measures and evaluates a diverse body shape analysis and physical fitness test and directs users to automated personalized exercise prescription which is prescribed by the expert system of three types of exercise templates: aerobics, anaerobics, and leisure sports. Automated personalized exercise prescriptions are built into XML based documents because the data needs to be in the form of flexible, expansible, and convertible structures in order to process various exercise templates, BIOFIT, a digital exercise trainer, monitors and provides feedback on the physiological parameters while users are working out in the gymnasium. If these parameters do not range within the prescribed target zone, the device will alarm users to control the exercise and make the exercise trainer adjust systemically the proper exercise level. Numeric health information such as the report of the physical fitness test and the exercise prescription makes people stay interested in exercising. In addition, this service can be delivered through the Internet.

• Advances in environmental research
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• v.6 no.4
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• pp.265-279
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• 2017

The climate change has made adverse effects on land surface temperature for many regions of the world. Several climatic studies focused on different downscaling techniques for climatological parameters of different regions. For statistical downscaling of any hydrological parameters, conventional Neural Network Models were used in common. However, it seems that in any modeling study, uncertainty is a vital aspect when making any predictions about the performance. In this paper, Gamma Test is performed to determine the data length selection for training to minimize the uncertainty in model development. Another measure to improve the data quality and model development are wavelet transforms. Hence, Gamma Test with Wavelet decomposed Feedforward Neural Network (GT-WNN) model is developed and tested for downscaled land surface temperature of Patna Urban, Bihar. The results of GT-WNN model are compared with GT-FFNN and conventional Feedforward Neural Network (FFNN) model. The effectiveness of the developed models is illustrated by Root Mean Square Error and Coefficient of Correlation. Results showed that GT-WNN outperformed the GT-FFNN and conventional FFNN in downscaling the land surface temperature. The land surface temperature is forecasted for a period of 2015-2044 with GT-WNN model for Patna Urban in Bihar. In addition, the significance of the probable changes in the land surface temperature is also found through Mann-Kendall (M-K) Test for Summer, Winter, Monsoon and Post Monsoon seasons. Results showed an increasing surface temperature trend for summer and winter seasons and no significant trend for monsoon and post monsoon season over the study area for the period between 2015 and 2044. Overall, the M-K test analysis for the annual data shows an increasing trend in the land surface temperature of Patna Urban.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.1
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• pp.253-256
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• 2014

Background: The standard treatment in the metastatic colorectal cancer consists of 5-FU based infusional regimens. However, with oral fluoropyrimidines, equal tumor responses may be obtained. Capecitabine causes macrocytosis of the cells by inhibition of DNA synthesis. In this context, a relationship was found between mean corpuscular volume (MCV) and response to therapy in breast cancer patients treated with Capecitabine, but whether this relationship also pertains in colorectal cancer has not been established. Materials and Methods: A total of 102 metastatic colorectal cancer patients treated with a oxaliplatin (XELOX)${\pm}$Bevacizumab combination were retrospectively evaluated. Patients were randomized into three groups. Hematological parameters (MCV, MPV, PCT, PLT, NLR) were recorded retrospectively, before treatment and after 3 cycles of chemotherapy. Results: After three cycles of therapy, 20 (19.6%) patients had progressive disease (PD), 41 (40.1%) had stable disease (SD), and 41 (40.1%) demonstrated a partial response (PR). In 62 (60.7%) treatment was with capesitabin plus XELOX therapy, and in 40 (39.2%) it was XELOX-Bevacizumab combination therapy. There was no difference among three groups before the treatment in terms of MCV, MPV, PCT, PLT, and NLR. MCV showed significant increase in chemotherapy response groups (PR and SD). In addition, a significant decrease was observed for platelet count in chemotherapy response groups. While NLR decrease was seen in only a PR group, PCT decrease was observed in all three groups. PCT and PLT values were higher in patients receiving Bevacizumab. Conclusions: PLT, PCT, MPV, and NLR values were decreased due to Capecitabine-based chemotherapy, however MCV was increased. PCT and PLT values were higher in patients who received Bevacizumab than those who did not. MCV, PLT, and NLR can be considered as important factors in predicting response to colorectal carcinoma treatment.

• The Journal of Korean Society for School & Community Health Education
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• v.2 no.2
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• pp.1-22
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• 2001

The purpose of this study was to evaluate the effect of moderate physical exercise program on a number of immune parameters in middle school boys($15.07{\pm}0.39$ yrs). 14 volunteers were included in this physical exercise program. They were randomly assigned to an experiment(7) and a control(7) group. Measurements were taken before program, after 4 weeks and 8 weeks of aerobic exercise for immune parameters such as number of circulating leukocytes, concentration of WEC subsets, lymphocyte subsets, immunoglobulins, complements, and number of blood adipose components. Aerobic exercise consisted of track running at a work intensity of 65% HRmax, $45{\sim}55$ min per day, 3 times per week, and for 8 weeks. The results are as follows; 1. There were no significant changes between and within groups in the number of circulating leukocytes and concentration of WEC subsets. 2. There were significant(p<.05) changes in concentration of B-cell between groups, and T-cell, helper T-cell, and B-cell within experimental group. 3. There were no significant changes between and within groups in concentration of complements. 4. There were significant(p<.05) changes in concentration of IgG between groups and within experimental group. 5. There were significant(p<.05) changes in concentration of TG between groups, and TG, HDL-C and LDL-C within experimental group. In conclusion, the moderate exercise training for 8 weeks can be beneficial on immune function and decrease the concentration of flood adipose components in adolescents.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.279-283
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• 2004

In general, triangular shape filters are used in the filter bank when we extract MFCC feature parameters from the spectrum of the speech signal. A different approach, which uses specific filter shapes in the filter bank that are optimized to the spectrum of training speech data, is proposed by Lee et al. to improve the recognition rate. A principal component analysis method is used to get the optimized filter coefficients. Using a large amount of 4-digit telephone speech database, in this paper, we get the MFCCs based on the PCA-optimized filter bank and compare the recognition performance with conventional MFCCs and direct weighted filter bank based MFCCs. Experimental results have shown that the MFCC based on the PCA-optimized filter bank give slight improvement in recognition rate compared to the conventional MFCCs but fail to achieve better performance than the MFCCs based on the direct weighted filter bank analysis. Experimental results are discussed with our findings.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.11
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• pp.108-115
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• 2009

In metal forming technologies, the stamping process is one of the significant manufacturing processes to produce sheet metal components. It is important to design stamping process which can produce sound products without defect such as fracture and wrinkle. The objective of this study is to propose the feasible formability diagram which denotes the safe region without fracture and wrinkle for effective design of stamping process. To determine the feasible formability diagram, FE-analyses were firstly performed for the combinations of process parameters and then the characteristic values for fracture and wrinkle were estimated from the results of FE-analyses based on forming limit diagram. The characteristic values were extended through training of the artificial neural network. The feasible formability diagram was finally determined for various combinations of process parameters. The stamping process of turret suspension to support suspension module was taken as an example to verify the effectiveness of feasible formability diagram. The results of FE-analyses for process conditions within fracture and wrinkle as well as safe regions were in good agreement with experimental ones.