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  • Introduction
  • Machine Learning
    • Recommended Courses
      • For Undergrad Research
      • Math for Machine Learning
    • ML Notes
      • Covariance Correlation
      • Feature Selection
      • Linear Regression
      • Entropy, Cross-Entropy, KL Divergence
      • Bayesian Classifier
        • Terminology Review
        • Bayesian Classifier for Normally Distributed classes
      • Linear Discriminant Analysis
      • Logistic Regression
        • Logistic Regression Math
      • Logistic Regression-MaximumLikelihood
      • SVM
        • SVM concept
        • SVM math
      • Cross Validation
      • Parameter, Density Estimation
        • MAP, MLE
        • Gaussian Mixture Model
      • E-M
      • Density Estimation(non-parametric)
      • Unsupervised Learning
      • Clustering
      • kNN
      • WaveletTransform
      • Decision Tree
    • Probability and Statistics for Machine Learning
      • Introduction
      • Basics of Data Analysis
      • Probability for Discrete Random Variable
      • Poisson Distribution
      • Chi-Square Distribution
      • P-value and Statistical Hypothesis
      • Power and Sample Size
      • Hypothesis Test Old
      • Hypothesis Test
      • Multi Armed Bandit
      • Bayesian Inference
      • Bayesian Updating with Continuous Priors
      • Discrete Distribution
      • Comparison of Bayesian and frequentist inference
      • Confidence Intervals for Normal Data
      • Frequenist Methods
      • Null Hypothesis Significance Testing
      • Confidence Intervals: Three Views
      • Confidence Intervals for the Mean of Non-normal Data
      • Probabilistic Prediction
  • Industrial AI
    • PHM Dataset
    • BearingFault_Journal
      • Support Vector Machine based
      • Autoregressive(AR) model based
      • Envelope Extraction based
      • Wavelet Decomposition based
      • Prediction of RUL with Deep Convolution Nueral Network
      • Prediction of RUL with Information Entropy
      • Feature Model and Feature Selection
    • TempCore Journal
      • Machine learning of mechanical properties of steels
      • Online prediction of mechanical properties of hot rolled steel plate using machine learning
      • Prediction and Analysis of Tensile Properties of Austenitic Stainless Steel Using Artificial Neural
      • Tempcore, new process for the production of high quality reinforcing
      • TEMPCORE, the most convenient process to produce low cost high strength rebars from 8 to 75 mm
      • Experimental investigation and simulation of structure and tensile properties of Tempcore treated re
    • Notes
  • LiDAR
    • Processing of Point Cloud
    • Intro. 3D Object Detection
    • PointNet
    • PointNet++
    • Frustrum-PointNet
    • VoxelNet
    • Point RCNN
    • PointPillars
    • LaserNet
  • Simulator
    • Simulator List
    • CARLA
    • Airsim
      • Setup
      • Tutorial
        • T#1
        • T#2
        • T#3: Opencv CPP
        • T#4: Opencv Py
        • Untitled
        • T#5: End2End Driving
  • Resources
    • Useful Resources
    • Github
    • Jekyll
  • Reinforcement Learning
    • RL Overview
      • RL Bootcamp
      • MIT Deep RL
    • Textbook
    • Basics
    • Continuous Space RL
  • Unsupervised Learning
    • Introduction
  • Unclassified
    • Ethics
    • Conference Guideline
  • FPGA
    • Untitled
  • Numerical Method
    • NM API reference
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  • ๊ฒฐ์ œ ๋ฐฉ๋ฒ•
  • ์ฐธ์„ ์ค‘
  • ์ฐธ์„ ํ›„

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  1. Unclassified

Conference Guideline

๊ฒฐ์ œ ๋ฐฉ๋ฒ•

๊ณผ์ œ ์˜ˆ์‚ฐ/ ๊ฒฐ์ œ ๋ฐฉ๋ฒ• ์€ ๋žฉ์žฅ์—๊ฒŒ ๋ฌธ์˜

  • ์ฐธ๊ฐ€๋น„ ๋ฐ ํšŒ์›๋น„: ํ•™์ƒ ํšŒ์›์œผ๋กœ ์‹ ์ฒญ, ๊ณผ์ œ ์นด๋“œ ์ข…๋ฅ˜ ๊ตฌ๋ถ„ ํ•„์š”

  • ์ถœ์žฅ๋น„: ํ›„๋ถˆ์ œ, ๊ณผ์ œ์— ๋”ฐ๋ผ ํ•„์š”ํ•œ ์ฆ๋น™ ์„œ๋ฅ˜ ์ˆ™์ง€

์ฐธ์„ ์ค‘

๊ด€์‹ฌ ๋ถ„์•ผ์˜ ํ•™ํšŒ ๋ฐœํ‘œ๋ฅผ ๋ฏธ๋ฆฌ ์„ ์ •ํ•˜์—ฌ ์ฒญ์ทจ ๋ฐ ๊ธฐ๋ก

  • ๊ด€์‹ฌ๋ถ„์•ผ ๊ตฌ๋‘ ๋ฐœํ‘œ ์ฒญ์ทจ: ์ค‘์š”ํ•œ ์ž๋ฃŒ๋Š” ์‚ฌ์ง„ ์ดฌ์˜ (์†Œ์Œ์œผ๋กœ ์„ธํŒ… ํ•„์š”)

  • ๊ด€์‹ฌ๋ถ„์•ผ ํฌ์Šคํ„ฐ ๋ฐœํ‘œ: ์ค‘์š”ํ•œ ์ž๋ฃŒ ์‚ฌ์ง„ ์ดฌ์˜

์ฐธ์„ ํ›„

๋…ผ๋ฌธ, ์‹ค์ • ๋ฐ ์ฆ๋น™์ž๋ฃŒ ์ •๋ฆฌ ํ•„์ˆ˜

  1. Research>Conference> ์—์„œ ํ•™ํšŒ๋ช…_์—ฐ๋„ ๋ช…์œผ๋กœ ์ƒˆ๋กœ์šด ํด๋” ์ƒ์„ฑ

    • (1) ํ•™์ˆ ๋Œ€ํšŒ ๋…ผ๋ฌธ ์›๋ณธ (*.docs ๋“ฑ), (2) ๊ตฌ๋‘๋ฐœํ‘œ/Poster๋ฐœํ‘œ ์›๋ณธ(PPT ๋“ฑ), (3) Conference Proceeding ๊ธฐ์žฌ๋œ ๊ณต์‹๋…ผ๋ฌธ ํŒŒ์ผ ์ €์žฅ

    • ํ•™ํšŒ ๋…ผ๋ฌธ ํฌ์Šคํ„ฐ ๋“ฑ ์‚ฌ์ง„์ดฌ์˜๋ณธ ์ €์žฅ

  2. ์—ฐ๊ตฌ์‹ค๊ณต์œ ํด๋”>์—ฐ๊ตฌ์‹ค_์‹ค์ ์ž๋ฃŒ ํด๋”์— ๊ณต์‹ ๋…ผ๋ฌธ ํŒŒ์ผ ์ €์žฅ

  • Conference> (ํ•ด์™ธ / ๊ตญ๋‚ด ํด๋” ์„ ํƒ)

  • Conference Proceeding ๊ธฐ์žฌ๋œ ๊ณต์‹๋…ผ๋ฌธ ํŒŒ์ผ ์ €์žฅ

4. ์ธ์ƒ๊นŠ์—ˆ๋˜ ํ•™ํšŒ ๋ฐœํ‘œ๋Š” ์—ฐ๊ตฌ์‹ค ๋žฉ๋ฏธํŒ…์—์„œ ์†Œ๊ฐœ

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3. (๊ตฌ๊ธ€ ์Šคํ”„๋ ˆ์‹œํŠธ)์— ํ•™์ˆ ๋Œ€ํšŒ ์ •๋ณด ๊ธฐ์žฌ

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