• The Journal of the Acoustical Society of Korea
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• v.41 no.6
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• pp.629-636
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• 2022

We present a preprocessing method for an underwater target detection model based on a convolutional neural network. The acoustic characteristics of the ship show ambiguous expression due to the strong signal power of the low frequency. To solve this problem, we combine feature preprocessing methods with various feature scaling methods and spectrogram methods. Define a simple convolutional neural network model and train it to measure preprocessing performance. Through experiment, we found that the combination of log Mel-spectrogram and standardization and robust scaling methods gave the best classification performance.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.66.1-66.1
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• 2021

Extinction curves observed toward individual Active Galactic Nuclei (AGN) usually show a steep rise toward Far-Ultraviolet (FUV) wavelengths and can be described by the Small Magellanic Cloud (SMC)-like dust model. This feature suggests the dominance of small dust grains of size a < 0.1 ㎛ in the local environment of AGN, but the origin of such small grains is unclear. In this paper, we aim to explain this observed feature by applying the RAdiative Torque Disruption (RATD) to model the extinction of AGN radiation from FUV to Mid-Infrared (MIR) wavelengths. We find that in the intense radiation field of AGN, large composite grains of size a > 0.1 ㎛ are significantly disrupted to smaller sizes by RATD up to dRATD > 100 pc in the polar direction and dRATD ~ 10 pc in the torus region. Consequently, optical-MIR extinction decreases, whereas FUV-near-Ultraviolet extinction increases, producing a steep far-UV rise extinction curve. The resulting total-to selective visual extinction ratio thus significantly drops to RV < 3.1 with decreasing distances to AGN center due to the enhancement of small grains. The dependence of RV with the efficiency of RATD will help us to study the dust properties in the AGN environment via photometric observations. In addition, we suggest that the combination of the strength between RATD and other dust destruction mechanisms that are responsible for destroying very small grains of a <0.05 ㎛ is the key for explaining the dichotomy observed "SMC" and "gray" extinction curve toward many AGN.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2788-2808
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• 2023

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1273-1283
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• 2019

This paper proposes an advanced crop classification model that combines a procedure for weighted combination of spatial features extracted from multi-scale input images with a conventional convolutional neural network (CNN) structure. The proposed model first extracts spatial features from patches with different sizes in convolution layers, and then assigns different weights to the extracted spatial features by considering feature-specific importance using squeeze-and-excitation block sets. The novelty of the model lies in its ability to extract spatial features useful for classification and account for their relative importance. A case study of crop classification with multi-temporal Landsat-8 OLI images in Illinois, USA was carried out to evaluate the classification performance of the proposed model. The impact of patch sizes on crop classification was first assessed in a single-patch model to find useful patch sizes. The classification performance of the proposed model was then compared with those of conventional two CNN models including the single-patch model and a multi-patch model without considering feature-specific weights. From the results of comparison experiments, the proposed model could alleviate misclassification patterns by considering the spatial characteristics of different crops in the study area, achieving the best classification accuracy compared to the other models. Based on the case study results, the proposed model, which can account for the relative importance of spatial features, would be effectively applied to classification of objects with different spatial characteristics, as well as crops.

• Journal of the Korean Academy of Esthetic Dentistry
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• v.24 no.2
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• pp.86-100
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• 2015

To harmonize with the remaining natural teeth a dentist and technician make an effort to do. Dental ceramic perfectly reproduce the functionality and esthetic is so moved that will deliver to the patient. However It is not easy to overcome the problem. Actually, it can't have the same spectrum curve between different object. The spectrum curve and reflectance is a unique feature of an object like fingerprints. So it is not that the identification of spectral curves that we usually focuses color. We need to understand the process of matamerism makes something like a combination of color perception. In other word that will tell in our field with ceramic teeth of the patient wish to match the color matching process to simulate the cone in our retinas with the same combination.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.272-278
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• 2015

This paper studied various parameter extraction methods using pitch information of speech for the development of the emotion recognition system. For this purpose, pitch parameters were extracted from korean speech database containing various emotions using stochastical information and numerical analysis techniques. GMM based emotion recognition system were used to compare the performance of pitch parameters. Sequential feature selection method were used to select the parameters showing the best emotion recognition performance. Experimental results of recognizing four emotions showed 63.5% recognition rate using the combination of 15 parameters out of 56 pitch parameters. Experimental results of detecting the presence of emotion showed 80.3% recognition rate using the combination of 14 parameters.

• Proceedings of the KIEE Conference
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• 2005.11a
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• pp.9-11
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• 2005

본 연구는 GIS 진단신뢰성 향상기술 개발을 목적으로, 16개의 인위적 결함을 이용하여 부분방전 신호를 발생시키고 검출하여 그 패턴인식 확률을 높이기 위하여 신경망에 Genetic Algorithm (GA) 을 적용하였다. 이를 위하여 다음과 같은 5가지 서로 다른 신경망 모델을 선택하였다: Back Propagation (BP), Jordan-Elman Network (JEN), Principal Component Analysis (PCA), Self-Organizing Feature Map (SOFM) 및 Support Vector Machine (SVM). 이와 같이 선택된 모델에 동일한 데이터를 학습 시키고 패턴인식 확률을 비교 및 분석하였다. 실험 결과에 의하면, BP의 인식률이 가장 높고 다음으로 JEN의 인식률이 높이 나타났으며, 후자의 경우 모든 결함에 대하여 정확한 패턴분류를 한 반면에 전자의 경우 1.8% 의 분류 오차가 발생하였다. 따라서 인식률이 높은 신경망이 더 정확한 패턴분류를 보장하지 못한다는 실험적 결과를 고려 할 때, 인식률이 높은 두 개의 모델을 선정하여 각각의 출력에 일정한 가중치를 주고 합산하여 새로운 출력을 얻는 방법을 제안한다.

• Journal of Korea Multimedia Society
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• v.9 no.3
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• pp.287-295
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• 2006

This paper compares two different approaches based on wavelet and Gabor decomposition towards representing the texture of an arbitrary region. The Gabor-domain mean and standard deviation combination is considered to be best in representing the texture of rectangular regions. However, texture representation of arbitrary regions would enable generalized object-based image retrieval and other applications in the future. In this study, we have found that the wavelet features perform better than the Gabor features in representing the texture of arbitrary regions. Particularly, the wavelet-domain standard deviation and entropy combination results in the best retrieval accuracy. Based on our experiment with texture image sets, we present and compare tile retrieval accuracy of multiple wavelet and Gabor feature combinations.

• Computational Structural Engineering
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• v.10 no.1
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• pp.185-191
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• 1997

Beam - and arch-like structures are two-dimensional bodies characterized by the fact of small thickness compared to the length of structures. Owing to this geometric feature, linear displacement approximations through the thickness such as Kirchhoff and Reissner-Mindlin theories which are more accessible one dimensional problems have been used. However, for accurate analysis of the behavior in the regions where the state of stresses is complex, two-dimensional linear elasicity or relatively high order of thickness polynomials is required. This paper analyses accuracy according to the order of thickness polynomials and introduces a technique for model combination for which several different polynomial orders are mixed in a single structure.

• Fashion & Textile Research Journal
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• v.15 no.3
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• pp.325-335
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• 2013

This work develops a motif design integrated with geometrical patterns in traditional ddeoksal and that can be applied to a scarf design so that traditional elements unique to Korean culture can be developed further for a modern application to various design fields. For the research method, literature reviews on op art and traditional ddeoksal were conducted with Adobe Illustrator CS3 and Adobe Photoshop CS3. As for the motif combination, such applications were taken as five pieces from the works of Victor Vasarely and some traditional ddeoksal shapes such as oblique line pattern, taegeuk pattern, and geometrical pattern. Abstract and geometrical images were borrowed from op art and ddeoksal for image expression. The total number of works selected was eleven. To realize the applied scarf design, a motif layout was performed with the scarf center or rim highlighted so that each design feature could be remarkable based on the motif combination. With the function of scaling, rotation, opacity control, filtering effect, the changed images were shown through motif distortion. In addition, this work applies a single combined motif to products for a possible transformation into handkerchiefs and boutique scarfs in the case of smaller sized scarfs.