• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.6
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• pp.77-82
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• 2005

This paper is proposed fuzzy neural network(FNN) and artificial neural network(ANN) based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed control and estimation of speed of induction motor using fuzzy and neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the experimental results to verify the effectiveness of the new method.

• Journal of Korea Game Society
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• v.18 no.1
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• pp.125-134
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• 2018

This paper presents a practical method to efficiently extract the rotational part of a $3{\times}3$ matrix that changes continuously in time. This is the key technique in the corotational FEM and the shape matching deformation popular in physics-based dynamic deformation. Recently, in contrast to the traditional polar decomposition methods independent of time, an iterative method was proposed that formulates the rotation extraction in a physics-based way and exploits an incremental representation of rotation. We develop an optimization method that reduces the number of iterations under the assumption that the maximum magnitude of the incremental rotation vector is limited within ${\pi}/2$. Realistic simulation of dynamic deformation employs a sufficiently small time step, and thus this assumption is not problematic in practice. We demonstrate the efficiency and practicality of our method in various experiments.

• The Journal of the Korea Contents Association
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• v.9 no.6
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• pp.266-274
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• 2009

An abdominal aortic aneurysm occurs most commonly in older individuals (between 65 and 75), and more in men and smokers. The most important complication of an abdominal aortic aneurysm is rupture, which is most often a fatal event. An abdominal aortic aneurysm weakens the walls of the blood vessel, leaving it vulnerable to bursting open, or rupturing, and spilling large amounts of blood into the abdominal cavity. surface modeling is very useful to surgery for quantitative analysis of abdominal aortic aneurysm. the 3D representation and surface modeling an abdominal aortic aneurysm structure taken from Multi Detector Computed Tomography. The construction of the 3D model is generally carried out by staking the contours obtained from 2D segmentation of each CT slice, so the quality of the 3D model strongly defends on the precision of segmentation process. In this work we present deformable model algorithm. deformable model is an energy-minimizing spline guided by external constraint force. External force which we call Gradient Vector Flow, is computed as a diffusion of a gradient vectors of gray level or binary edge map derived from the image. Finally, we have used snakes successfully for abdominal aortic aneurysm segmentation the performance of snake was visually and quantitatively validated by experts.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.626-626
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• 2015

본 연구에서는 하천공간정보의 웹서비스를 위해 SOAP(Simple Object Access Protocol) API 및 REST(Representation State Transfer) API로 제공하는 HydroG-OneFlow 웹서비스를 개발하였다. HydroG-OneFlow는 GML 기반의 서비스를 제공하며 GetBasin, GetGeoVariable 및 GetData 등의 기본서비스로 구성된다. GML은 GIS S/W의 벡터 GML 포맷과 공간정보 오픈플랫폼 서비스인 브이월드 데이터 API에서 제공하는 GML 포맷을 참고하여 하천공간 벡터정보를 제공할 수 있도록 GML을 구성하였다. GDM 공간 데이터에 대한 벡터정보 ML 수용 수준을 향상시킬 수 있도록 벡터구조의 점, 선, 면 정보에 대하여 GML의 PointPropertyType, CurvePropertyType, SurfacePropertyType을 도입하였다. 또한 일반적인 공간자료에서는 Multi 객체에 대한 지원도 필요하다. 현 GDM 데이터베이스에서도 OGC 표준의 MultiPoint, MultiLineString, MultiPolygon을 지원하고 있다. 이를 위하여 GML의 상응 요소인MultiPointPropertyType, MultiCurvePropertyType, MultiSurfacePropertyType을 하천공간정보 벡터 스키마에 도입하여 활용하였다. 클라이언트 서버 통신은 메시지 교환프로토콜인 SOAP을 사용하여 서버의 객체를 직접 호출하여 이루어진다. 서버는 서버의 제공 서비스를 WSDL(Web Service Description Language)를 통하여 게시하고 클라이언트는 이 기준(Criteria)을 참고하여 접근한다. GetData의 경우 Type(GRID or VECTOR), GDM(Geospatial Data Model) 여부(true or false), LayerName, BasinID, GenTime을 인자로 받아 GeoData에서 검색된 정보를 반환한다. SOAP버전은 1.1과 1.2를 지원하여 접근하는 클라이언트에서 선택할 수 있도록 개발하였다.

• Journal of the Korean Mathematical Society
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• v.58 no.3
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• pp.643-702
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• 2021

Fix ℤ/2 is the prime field of two elements and write 𝒜₂ for the mod 2 Steenrod algebra. Denote by GL_d := GL(d, ℤ/2) the general linear group of rank d over ℤ/2 and by ${\mathfrak{P}}_d$ the polynomial algebra ℤ/2[x₁, x₂, …, x_d] as a connected unstable 𝒜₂-module on d generators of degree one. We study the Peterson "hit problem" of finding the minimal set of 𝒜₂-generators for ${\mathfrak{P}}_d$. Equivalently, we need to determine a basis for the ℤ/2-vector space $$Q{\mathfrak{P}}_d:={\mathbb{Z}}/2{\otimes}_{\mathcal{A}_2}\;{\mathfrak{P}}_d{\sim_=}{\mathfrak{P}}_d/{\mathcal{A}}^+_2{\mathfrak{P}}_d$$ in each degree n ≥ 1. Note that this space is a representation of GL_d over ℤ/2. The problem for d = 5 is not yet completely solved, and unknown in general. In this work, we give an explicit solution to the hit problem of five variables in the generic degree n = r(2^t - 1) + 2^ts with r = d = 5, s = 8 and t an arbitrary non-negative integer. An application of this study to the cases t = 0 and t = 1 shows that the Singer algebraic transfer of rank 5 is an isomorphism in the bidegrees (5, 5 + (13.2⁰ - 5)) and (5, 5 + (13.2¹ - 5)). Moreover, the result when t ≥ 2 was also discussed. Here, the Singer transfer of rank d is a ℤ/2-algebra homomorphism from GL_d-coinvariants of certain subspaces of $Q{\mathfrak{P}}_d$ to the cohomology groups of the Steenrod algebra, $Ext^{d,d+*}_{\mathcal{A}_2}$ (ℤ/2, ℤ/2). It is one of the useful tools for studying these mysterious Ext groups.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.69.2-69.2
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• 2019

To construct a coronal force-free magnetic field, we must impose the boundary normal current density (or three components of magnetic field) as well as the boundary normal field at the photosphere as boundary conditions. The only method that is known to implement these boundary conditions exactly is the method devised by Grad and Rubin (1958). However, the Grad-Rubin method and all its variations (including the fluxon method) suffer from convergence problems. The magnetofrictional method and its variations are more robust than the Grad-Rubin method in that they at least produce a certain solution irrespective of whether the global solution is compatible with the imposed boundary conditions. More than often, the influence of the boundary conditions does not reach beyond one or two grid planes next to the boundary. We have found that the 2D solenoidal gauge condition for vector potentials allows us to implement the required boundary conditions easily and effectively. The 2D solenoidal condition is translated into one scalar function. Thus, we need two scalar functions to describe the magnetic field. This description is quite similar to the Chandrasekhar-Kendall representation, but there is a significant difference between them. In the latter, the toroidal field has both Laplacian and divergence terms while in ours, it has only a 2D Laplacian term. The toroidal current density is also expressed by a 2D Laplacian. Thus, the implementation of boundary normal field and current are straightforward and their effect can permeate through the whole computational domain. In this paper, we will give detailed math involved in this formulation and discuss possible lateral and top boundary conditions and their meanings.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.942-943
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• 2021

The present work proposes a classification system for the HEp-2 cell images using an unsupervised deep feature learning method. Unlike most of the state-of-the-art methods in the literature that utilize deep learning in a strictly supervised way, we propose here the use of the deep convolutional autoencoder (DCAE) as the principal feature extractor for classifying the different types of the HEp-2 cell images. The network takes the original cell images as the inputs and learns to reconstruct them in order to capture the features related to the global shape of the cells. A final feature vector is constructed by using the latent representations extracted from the DCAE, giving a highly discriminative feature representation. The created features will be fed to a nonlinear classifier whose output will represent the final type of the cell image. We have tested the discriminability of the proposed features on one of the most popular HEp-2 cell classification datasets, the SNPHEp-2 dataset and the results show that the proposed features manage to capture the distinctive characteristics of the different cell types while performing at least as well as the actual deep learning based state-of-the-art methods.

• Land and Housing Review
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• v.14 no.4
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• pp.95-102
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• 2023

Numerous investigations have explored the integration of machine leaning algorithms with high-resolution drone image for object detection in urban settings. However, a prevalent limitation in vehicle extraction studies involves the reliance on bounding boxes rather than instance segmentation. This limitation hinders the precise determination of vehicle direction and exact boundaries. Instance segmentation, while providing detailed object boundaries, necessitates labour intensive labelling for individual objects, prompting the need for research on automating unsupervised instance segmentation in vehicle extraction. In this study, a novel approach was proposed for vehicle extraction utilizing unsupervised SVM classification applied to vehicle bounding boxes in drone images. The method aims to address the challenges associated with bounding box-based approaches and provide a more accurate representation of vehicle boundaries. The study showed promising results, demonstrating an 89% accuracy in vehicle extraction. Notably, the proposed technique proved effective even when dealing with significant variations in spectral characteristics within the vehicles. This research contributes to advancing the field by offering a viable solution for automatic and unsupervised instance segmentation in the context of vehicle extraction from image.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

• The Journal of the Acoustical Society of Korea
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• v.23 no.1
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• pp.8-16
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• 2004

An inversion method is presented for the determination of the compressional wave speed, compressional wave attenuation, thickness of the sediment layer and density as a function of depth for a horizontally stratified ocean bottom. An experiment for estimating those properties was conducted in the shallow water of South Sea in Korea. In the experiment, a light bulb implosion and the propagating sound were measured using a VLA (vertical line array). As a method for estimating the geoacoustic properties, a coherent broadband matched field processing combined with Genetic Algorithm was employed. When a time-dependent signal is very short, the Fourier transform results are not accurate, since the frequency components are not locatable in time and the windowed Fourier transform is limited by the length of the window. However, it is possible to do this using the wavelet transform a transform that yields a time-frequency representation of a signal. In this study, this transform is used to identify and extract the acoustic components from multipath time series. The inversion is formulated as an optimization problem which maximizes the cost function defined as a normalized correlation between the measured and modeled signals in the wavelet transform coefficient vector. The experiments and procedures for deploying the light bulbs and the coherent broadband inversion method are described, and the estimated geoacoustic profile in the vicinity of the VLA site is presented.