발전기금
For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| CHS2003 | Robust System Design with Big Data Analytics and Artificial Intelligence | 2 | 4 | Major | Bachelor | 1-4 | Challenge Semester | - | No |
| In this course, the fundamental theories and methodologies on big-data analytics and artificial intelligence (AI) algorithms for prognostics and health management (PHM) of engineering systems are mainly covered. More specifically, the reliability analysis, sensor-based big-data collection, signal processing, statistical feature extraction and selection, and AI-based modeling are studied, and the hands-on practices are also carried out. In addition, various case examples are introduced to study the robust engineering system design using the big-data analytics and AI algorithms. | |||||||||
| CHS2017 | A new human, phono sapiens Experience Design | 3 | 6 | Major | Bachelor | 1-4 | Challenge Semester | - | No |
| As humans started using smartphones, they are experiencing changes in consumption psychology, consumer behavior, and market ecosystems due to rapidly changing lifestyles. This represents a new type of human, the main protagonist of the revolution, called Phono Sapiens. As consumption civilization changes, we learn about digital transformation and changes in business models driven by the development and evolution of big data, artificial intelligence, and digital platforms. We analyze and learn the direction of digital experience design (Digital Experience Design) based on digital transformation. Companies provide and understand the direction of new business innovation and change in accordance with rapidly changing trends for Phono Sapiens, the new consumers. | |||||||||
| CHS7002 | Machine Learning and Deep Learning | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| This course covers the basic machine learning algorithms and practices. The algorithms in the lectures include linear classification, linear regression, decision trees, support vector machines, multilayer perceptrons, and convolutional neural networks, and related python pratices are also provided. It is expected for students to have basic knowledge on calculus, linear algebra, probability and statistics, and python literacy. | |||||||||
| CHS7003 | Artificial Intelligence Application | 3 | 6 | Major | Bachelor/Master/Doctor | Challenge Semester | - | No | |
| Cs231n, an open course at Stanford University, is one of the most popular open courses on image recognition and deep learning. This class uses the MOOC content which is cs231n of Stanford University with a flipped class way. This class requires basic undergraduate knowledge of mathematics (linear algebra, calculus, probability/statistics) and basic Python-based coding skills. The specific progress and activities of the class are as follows. 1) Listening to On-line Lectures (led by learners) 2) On-line lecture (English) Organize individual notes about what you listen to 3) On-line lecture (English) QnA discussion about what was listened to (learned by the learner) 4) QnA-based Instructor-led Off-line Lecture (Korean) Lecturer 5) Team Supplementary Presentation (Learner-led) For each topic, learn using the above mentioned steps from 1) to 5). The grades are absolute based on each activity, assignment, midterm exam and final project. Class contents are as follows. - Introduction Image Classification Loss Function & Optimization (Assignment # 1) - Introduction to Neural Networks - Convolutional Neural Networks (Assignment # 2) - Training Neural Networks - Deep Learning Hardware and Software - CNN Architectures-Recurrent Neural Networks (Assignment # 3) - Detection and Segmentation - Generative Models - Visualizing and Understanding - Deep Reinforcement Learning - Final Project. This class will cover the deep learning method related to image recognitio | |||||||||
| DES4001 | Convergence Capstone Design | 3 | 6 | Major | Bachelor/Master | Design | Korean | Yes | |
| Various students from different majors, Design, Art, IT, Business, Engineering, and etc., are gathered to study the development of future new technology, services and creative design products. Also, they are processing the prototype of the study and supporting the application of effective ideas. The purposes of this study are to overcome the present level of studies' approaches and create new and innovative values and to acquire creativeness, Problem Based Learning skill, and ability to conduct Team Project. | |||||||||
| EAM7001 | Plasma Processes and Equipment | 3 | 6 | Major | Bachelor/Master/Doctor | 3-4 | Advanced Materials Science and Engineering | Korean | Yes |
| This class will discuss theoretical and experimental backgrounds on processing, diagnostic, and equipment technologies related to plasma deposition and etching applied to semiconductor, displays, and various nanodevice processing. The contents are as follows; 1) Gas Collision Processes, 2) Vacuum and Parts, 3) Plasma Technology, 4) DC, RF, High Density Plasmas, 5) Plasma Dignostics, 6) Plasma Deposition, 7) Plasma Etching, 8) Seminar on Recent Plasma Application Technologies | |||||||||
| ECE4247 | Power Electronics System Analysis | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | - | No |
| Inverters and converters play an important role to operate fuel cell systems, hybrid electric vehicles, and etc. In this subject, design and control of various power conversion circuits according to the application conditions. Completing this subject, one can handle hardwares and softwares for power conversion circuits and can utilize the theory and technique for industry applications | |||||||||
| ECE4249 | Computer Vision | 3 | 6 | Major | Bachelor/Master | 1-4 | Electrical and Computer Engineering | Korean | Yes |
| This course focuses in the study of theories for image analysis. The first part consists of Image formulation model, early processing, boundary detection, region growing and segmentation, motion detection, merging and introduction of morphology. The second part, we cover basic concepts of statistical model, dis- criminant function, decision boundary and rules and neural network for visual pattern recognition. | |||||||||
| EEE3006 | Modern Optics | 3 | 3 | Major | Bachelor | 3-4 | Electronic and Electrical Engineering | Korean | Yes |
| The goal of this class is to understand the propagation of light in free space and in materials. The light propagation is first explained by the wave theory or Huygens's principle. Then the principles of reflection and refraction are explained. Next the light propagation is explained by the oscillating dipoles of atoms. Based on this theory the light scattering is explained. Next the operation of simple optical systems such as simple lens system, compound lens system, camera and human eyes is explained. | |||||||||
| EME2006 | Computer Aided Drawing | 1 | 2 | Major | Bachelor | 2-4 | English | Yes | |
| The main objective of the course is to give the basic knowledge of mechanical drawing. The lecture on how to convert the idea in mind into the drawing is delivered and practices with softwares and drawing tools are performed. | |||||||||
| EME2007 | Engineering Thermodynamics | 3 | 6 | Major | Bachelor | 2-4 | English,Korean | Yes | |
| The aim of the engineering thermodynamics is to estimate the amount and the quality of the stored, converted, and transported energy since the engineering thermodynamics is dealing with the fields related with energy and materials. The engineering thermodynamics deals with the first and second laws of thermodynamics based on the fundamental concepts of energy, entropy and properties in order to effectively utilize the energy. Since the engineering thermodynamics contains lots of conceptual facts compaining with the other mechanics related with the mechanical engineering, the continuous study and the solving of practical problems are required. | |||||||||
| EME2008 | Fluid Mechanics | 3 | 6 | Major | Bachelor | 2-4 | English,Korean | Yes | |
| Basic principles of fluid mechanics : basic concepts, properties of fluid, streamlines and pathlines, fluid statics, control volume approach, continuity equation, momentum equation, energy equation, Bernoulli's equation, dimensional analysis and similitude, pipe flows, and utility of the fundamental equations in describing various physical situations. | |||||||||
| EME2009 | Dynamics | 3 | 6 | Major | Bachelor | 2-4 | English | Yes | |
| Dynamics is an important branch in mechanical engineering, which studies the relations between force and motion. Basic dynamic theory and its applications are studied. Particle dynamics and rigid body dynamics will be studied with those kinematics for motion itself and kinematics for the relationship between motion and force. | |||||||||
| EME2012 | Solid Mechanics | 3 | 6 | Major | Bachelor | 2-4 | English,Korean | Yes | |
| Solid mechanics is a branch of applied mechanics that deals with the behavior of solid bodies subjected to various types of loading. The solid bodies considered in this course are axially loaded members, shafts in torsion, thin shells, beams, and columns, as well as structures that are assemblies of these components. The objective of this course is determination on stresses, strains, and displacements produced by the loads. | |||||||||
| EME2013 | Mechanical Engineering Materials | 3 | 6 | Major | Bachelor | 2-4 | English,Korean | Yes | |
| As the fundamental course in mechanical engineering, both theoretical and applied lectures are given to provide the basic knowledge necessary for the effective material selection and design of the machinery components and structures. Focused on metallic materials, the crystal structures, defects and strength, deformation, fracture, heat treatments, phase diagrams, strengthening mechanisms, the mechanical properties and characteristics of various steels, cast irons and nonferrous alloys will be discussed. | |||||||||