• 대한수학회지
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• 제57권3호
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• pp.655-668
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• 2020

In this paper, we establish the generalized Cameron-Storvick type theorem on function space. We then give relationships involving the generalized Cameron-Storvick type theorem, modified generalized integral transform and modified convolution product. A motivation of studying the generalized Cameron-Storvick type theorem is to generalize formulas and results with respect to the modified generalized integral transform on function space. From the some theories and formulas in the functional analysis, we can obtain some formulas with respect to the translation theorem of exponential functionals.

• 대한수학회보
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• 제52권6호
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• pp.1925-1935
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• 2015

In this paper, we study right half-plane harmonic mappings $f_0$ and f, where $f_0$ is fIxed and f is such that its dilatation of a conformal automorphism of the unit disk. We obtain a sufficient condition for the convolution of such mappings to be convex in the direction of the real axis. The result of the paper is a generalization of the result of by Li and Ponnusamy [11], which itself originates from a problem posed by Dorff et al. in [7].

• 한국정보통신학회논문지
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• 제8권1호
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• pp.41-48
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• 2004

본 논문에서는 채널의 진폭 특성만을 고려한 실내 무선 페이딩 환경에서 PPM 변조된 UWB 통신 시스템의 채널 성능을 분석하였다. 페이딩 채널은 기존에 발표되었던 UWB 전파 실험을 통한 데이터를 바탕으로 Nakagami-m분포 모델을 이용하여 페이딩 지수 m에 따른 다양한 채널 환경을 고려하였다. 또한, 채널 부호화 기법으로써 강력한 에러 정정 능력을 가진 컨벌루션 부호화 기법을 적용함으로써 페이딩으로 인해 열화된 시스템 성능을 개선하였다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2003년도 추계종합학술대회
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• pp.928-931
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• 2003

본 논문에서는 고속 광대역의 정보신호를 효율적으로 전송하기 위한 무선 LAN 채널환경에서 OFDM 전송방식을 사용할 때 반송파의 주파수 오프셋이 발생하는 동기 오차가 수신시스템에 미치는 영향으로 인한 수신 성능을 분석하였다. 성능개선 기법으로 Convolution Coding 기법을 적용함으로써 반송파 주파수 오프셋에 따른 성능 열화를 보상하였다. 성능 해석 결과, OFDM 시스템에서 주파수 오프셋이 커짐에 따라 성능이 열화됨을 알 수 있었고, (BER=$10^{-3}$)를 목표로 하는 경우 OFDM 시스템에 Convolution Coding 기법을 적용함으로써 성능 개선이 나타났지만 64QAM방식인 경우 주파수 오프셋 0.05, 0.1에서 주파수 오프셋에 의한 캐리어간 간섭의 영향이 지배적임을 알 수 있다.

• 한국통신학회논문지
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• 제27권3B호
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• pp.251-258
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• 2002

이 논문에서는 여러 반송파 직접수열 부호분할 다중접속 시스템에서 적응 부호율 길쌈부호화 방법을 살펴본다. 여러 가지 부호율을 쉽게 다를 수 있고 부호기와 복호기 얼개가 간단하도록 부호율 호환 구멍 뚫은 길쌈부호를(rate compatible punctured convolutional code: RCPC code) 쓴다. 데이터 처리량이 가장 많아지도록, 신호 대간섭과 잡음비 추정을 바탕으로 하는 적응 부호율 시스템을 제안한다. 제안한 적응 부호율 여러 반송파 직접수열부호분할 다중접속 시스템을 쓰면 주파수 대역 효율을 높이고 주파수 다양성을 얻을 수 있음을 보인다.

• 전기학회논문지
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• 제63권5호
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• pp.690-695
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• 2014

This paper presents a modified CCI image interpolation method based on the even-odd decomposition (EOD). The CCI method is a well-known technique to interpolate images. Although the method provides better image quality than the linear interpolation, its complexity still is a problem. To remedy the problem, this paper introduces analysis on the EOD decomposition of CCI and then proposes a reduced CCI interpolation in terms of complexity, providing better image quality in terms of PSNR. To evaluate the proposed method, we conduct experiments and complexity comparison. The results indicate that our method do not only outperforms the existing methods by up to 43% in terms of MSE but also requires low-complexity with 37% less computing time than the CCI method.

• 대한수학회지
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• 제49권4호
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• pp.671-686
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• 2012

This paper mainly deals with the tilted Carath$\acute{e}$odory class by angle ${\lambda}$ ${\in}$ ($-{\pi}/2$, ${\pi}/2$), denoted by $P{\lambda}$) an element of which maps the unit disc into the tilted right half-plane {<${\omega}$ : Re $e^{i{\lambda}}{\omega}$ > 0}. Firstly we will characterize $P{\lambda}$ from different aspects, for example by subordination and convolution. Then various estimates of functionals over $P{\lambda}$ are deduced by considering these over the extreme points of $P{\lambda}$ or the knowledge of functional analysis. Finally some subsets of analytic functions related to $P{\lambda}$ including close-to-convex functions with argument ${\lambda}$, ${\lambda}$-spirallike functions and analytic functions whose derivative is in $P{\lambda}$ are also considered as applications.

• Steel and Composite Structures
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• 제29권6호
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• pp.759-770
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• 2018

In the present investigation, by using the two numerical methods, free vibration analysis of laminated annular and annular sector plates have been studied. In order to obtain the main equations two different shell theories such as Love's shell theory and first-order shear deformation theory (FSDT) have been used for modeling. After obtaining the fundamental equations in briefly, the methods of harmonic differential quadrature (HDQ) and discrete singular convolution (DSC) are used to solve the equation of motion. Accuracy, convergence and reliability of the present HDQ and DSC methods were tested by comparing the existing results obtained by different methods in the literature. The effects of some geometric and material properties of the plates are investigated via these two methods. The advantages and accuracy of the HDQ and DSC methods have also been examined with different grid numbers and shell theory. Some results for laminated annular plates and laminated circular plates were also been supplied.

• 전력전자학회논문지
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• 제27권1호
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.265-271
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• 2022

Social media is a form of communication based on the internet to share information through content and images. Their choice of profile images and type of image they post can be closely connected to their personality. The user posted images are designated as personality traits. The objective of this study is to predict five factor model personality dimensions from profile images by using deep learning and neural networks. Developed a deep learning framework-based neural network for personality prediction. The personality types of the Big Five Factor model can be quantified from user profile images. To measure the effectiveness, proposed two models using convolution Neural Networks to classify each personality of the user. Done performance analysis among two different models for efficiently predict personality traits from profile image. It was found that VGG-69 CNN models are best performing models for producing the classification accuracy of 91% to predict user personality traits.