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Term Project Presentation on Dec. 6

(To volunteer, drag and drop your slides as images

by 23:00 pm, Sat, Nov. 26, 2022.)

6-page limit including the title page.

You will later have a chance to modify your slides

if selected to present.

DO NOT MAKE DOCUMENT-WIDE CHANGES SUCH AS CHANGING THE MASTER BACKGROUND!

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Student Name : 이선준

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Application of Mathematics in Noise Reduction

Department of physics&astronomy

이선준

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Classification of Signal and Noise

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Noise Reduction

Gaussian Noise

Impulsive Noise

Small amplitude

Large amplitude

Wave-like features(periodic, etc.)

Delta function-like features

Fourier Transformation(fft)

Convolution(filter)

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Gaussian Noise and Fourier Transformation

fft

ifft

Noise reduction

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Impulsive Noise

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Filter and Convolution

Median Filter applied

Average Filter applied

Gaussian Filter applied

Impulse Noise

Gaussian Noise

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Student Name : 장상욱

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Student Name : 김준모

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Various Applications of Complex Numbers and Variables

2022-18822 김준모

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Complex Number and Complex Plane

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Euler’s Formula

Ex) AC Circuit Analysis, Analysis of Waves,

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Complex Variable and Laplace Equation

  • z=x+iy

  • Cauchy-Riemann Relation(Holomorphic Function):

  • Laplace’s Equation in 2D

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Laplace’s Equation in Physics

  • Irrotational flow(incompressible fluid)
  • continuity equation:

  • Electrostatics

, if ,

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Quaternions

  • Complex number ⇔ 2D vectors, Higher dimension?
  • Euler’s four-square identity

  • Definition

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Student Name : 안수혁

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Student Name : 정재훈

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Student Name : 김도원

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Alternative Form of the Laplace Transform and Its Inverse

Doweon Kim

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Introduction

ODE를 푸는 적분변환이 가져야 할 성질들:

  1. 적분의 수렴성
  2. 역변환의 존재성
  3. f’(t)의 변환을 f(t)의 변환 꼴로
  4. 계산의 편의상 1차식 이용

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Interval of Integration

Ex) T1 transform에서 h(p) 결정

IC term

Laplace 변환의 단순 평행이동

모든 함수에 대해 변환값이

모든 함수에 대해 변환값이

적절

나머지 경우의 h(p)

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T2 transform

t0=0일 때 단순한 형태

지수 항이 간단, 부분적분 쉬움

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Solving ODE by using T2 transform

Ex)

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Inverse?

Leibniz rule로 역변환을 구할 수 있을까?

실패: 더 복잡한 함수의 역변환을 구하는 문제로 환원

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Student Name : 임수빈

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Student Name : 정승호

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Deep Learning in Astronomical Data Analysis

SUNGHO JUNG

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Deep Learning in Astronomical Data Analysis, SUNGHO JUNG

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Data Dilemma

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Deep Learning in Astronomical Data Analysis, SUNGHO JUNG

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Deep Learning in Astronomical Data

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Deep Learning in Astronomical Data Analysis, SUNGHO JUNG

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Example I: Galaxy Classification

Problem of overfitting (Donglin Wu et al., 2022)

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Deep Learning in Astronomical Data Analysis, SUNGHO JUNG

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Example II: Supernova Detection

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Deep Learning in Astronomical Data Analysis, SUNGHO JUNG

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Example III: Quasar Survey

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Deep Learning in Astronomical Data Analysis, SUNGHO JUNG

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Student Name : 김진수

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지금 TeX로 Term Paper 작업중이라, ppt는 시간 되는 대로 업데이트 하겠습니다.

아래는 작업중인 Term Paper 초록 및 목차입니다. (세부 내용은 아직 작성중입니다!)

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그람-슈미츠 직교화 알고리즘 최적화

전자기유도 실험에서 Magnetic Monopole과 Magnetic Dipole 모형의 차이

-지난 학기 물리학 실험 결과와 함께

아래 두 가지 주제로 진행 해 보았습니다.

둘 다 크게 재미있는 결과로 이어지지는 못했지만, 이런걸 해 봤다고 얘기는 해 보고 싶습니다.

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Student Name : 장준화

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Student Name : 서동혁

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Student Name : 가태현

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Student Name : 서동하

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Student Name : 윤해리

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Student Name : 배윤진

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Student Name : 박서연

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Student Name : 박준섭

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Student Name : 박연우

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Student Name : 이현진

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Student Name : 이상인

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Student Name : 유호진

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Student Name : 김주원

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Student Name : 박영민

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Student Name : 정환희

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