For more details on the courses, please refer to the Course Catalog
| Code | Course Title | Credit | Learning Time | Division | Degree | Grade | Note | Language | Availability |
|---|---|---|---|---|---|---|---|---|---|
| SFC5013 | Manufacturing Process Control | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This course explores statistical modeling and control in manufacturing processes. Topics include the use of experimental design and response surface modeling to understand manufacturing process physics, as well as defect and parametric yield modeling and optimization. Various forms of process control, including statistical process control, run by run and adaptive control, and real-time feedback control, are covered. Application contexts include several manufacturing processes. | |||||||||
| SFC5014 | Industrial Networking Applications | 3 | 6 | Major | Master/Doctor | - | No | ||
| Industrial networking applications are undergoing a major transformation, as the need for interfacing is increasing in various phases of industrial process such as robots, material handling, manufacturing and other control process. This trend is enabling unprecedented levels of automation, not just at the factory level, but throughout business processes. This course covers the standard networking devices and protocols in industry. Networking of PLCs is commonly used in industries for controlling multiple processes. Students get maximum practical exposure to networking devices and configuration of custom networks. | |||||||||
| SFC5015 | Industrial Artificial Intelligence | 3 | 6 | Major | Master/Doctor | - | No | ||
| Artificial Intelligence (AI) is a research field that studies how to realize intelligent human behavior on a computer. The ultimate goal of AI in the industry is to create computers that can learn, plan and solve problems autonomously. AI has been studied for more than half a century, but it is not possible to make computers as intelligent as humans in every way. But there are many successful applications. In some cases, computers with AI technology can be much smarter than we are. AI's main research topics in industry include problem solving, reasoning, planning, understanding natural language, computer vision, automatic programming, and machine learning. Of course, these themes are closely related to each other. For example, knowledge gained from learning can be used for both problem solving and reasoning. Indeed, problem-solving skills must be acquired through learning. Problem solving is also useful for reasoning and planning. In addition, methods developed in the field of pattern recognition can be used to solve both natural language understanding and computer vision. This course studies the most basic knowledge of AI understanding in industry. Students will learn some basic search algorithms such as knowledge representation and reasoning, pattern recognition, fuzzy logic, and neural networks for problem solving. | |||||||||
| SFC5016 | Smart Factory Consulting | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| In this course, the manufacturer learns how to develop a business plan and consulting on the level of field-oriented diagnostics considering the existing operating environment in order to be competitive in smart manufacturing. Understanding of smart factory construction throughout data collection, analysis, and diagnosis from facilities and equipment, proposal of customized system to manufacturers for new introduction, configuration and construction of collection environment for production site data using existing equipment and equipment, and diagnosis of problems and improvements. | |||||||||
| SFC5017 | Smart Factory Implementation TechnologySeminar | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| In this course, the latest issues in smart factory implementation technology and specialized knowledge for manufacturing innovation are acquired through seminars by external experts. The key to the innovation in manufacturing is to implement a smart factory suitable for the manufacturing site. In order to improve the productivity of industrial sites, building an autonomous factory in which various technologies such as artificial intelligence and IoT are converged is now a necessity rather than an option. Accordingly, the use of advanced technologies in the field of smart factory realization technology will increase. In this course, students learn how to apply and apply advanced manufacturing technologies as well as market and technology trends of smart factories. | |||||||||
| SFC5018 | Smart Factory ConvergenceTechnologySeminar | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| In this course, you will learn the latest issues in the field of smart factory convergence technology and expertise related to the application of intelligent information technology through seminars of external experts. Futuristic manufacturing system that combines smart factory convergence technologies such as cloud/edge computing, IoT sensor data, digital twin, AI-based vision, and RPA to the existing manufacturing environment to maximize data value, optimize operations, and integrate quality management It is important. This course highlights understanding and prospects for smart factories, analyzes major convergence technologies, and learns strategies and methodologies for developing various digital manufacturing systems and solutions. | |||||||||
| SFC5019 | Manufacturing Big-data Analytics | 3 | 6 | Major | Master/Doctor | - | No | ||
| This course deals with a broad outline of big data technologies. In order to understand the basic fundamentals of big data technologies, this course firstly introduces the state-of-the-art big data technologies which are currently being developed. In addition, the principles of HDFS, Map Reduce, and NoSQL are introduced. This course also presents the recent development trend of real-time big data processing such as Storm, Kafka, and Spark Streaming. The course has an additional focus on Machine Leaning & Data Science developed to advance data science skills that are often required to implement big data projects in manufacturing. Finally, each student designs and implement a big data system in order to resolve a given problem in manufacturing. | |||||||||
| SFC5020 | Special Theory of Smart Factory Engineering | 3 | 6 | Major | Master/Doctor | Korean | Yes | ||
| This course covers the overall content of smart factories, separated by major disciplines. To secure sustainable competitiveness in future industries, manufacturing processes must be clearly and uniquely recognized as digital networking, and each process must communicate on its own and be interconnected. To this end, the relevant knowledge shall be acquired and applied to the site to discuss ways to create new values. In addition, research trends and industrial trends are investigated and learned in major fields such as AI, big data, cloud computing, industrial Internet of Things, manufacturing data platform, and manufacturing technology. | |||||||||
| SFC5022 | Internet Technology for Industrial Things | 3 | 6 | Major | Master/Doctor | - | No | ||
| Theory of general terms in this subject is being put to use the Internet of Things in industry technology and introduces the application. Internet of Things, in particular, structures, various objects for Internet services, Internet of Things, service development through the Internet of Things and simple Internet of Things to introduce application development.Learn how. In addition, network, devices, the network, application technology platforms and services in order to learn and understand the project different devices and platforms.Learn the skills needed to deploy the platform by industry. | |||||||||
| SFC5023 | Machine Vision Application | 3 | 6 | Major | Master/Doctor | 1-4 | Korean | Yes | |
| This course focuses on industrial applications of machine vision. Basic digital image processing and machine vision concepts and their applications to robotics and automation are covered. Topics include digital image processing, image formation, two-dimensional transformation, boundary descriptors, motion, camera calibration, vision for robot control, 3D vision, and hardware architectures supporting vision. In addition, application cases of the manufacturing environment are introduced and industrial problem solving projects are carried out. | |||||||||
| SFC5024 | Deep Learning Technologies Application | 3 | 6 | Major | Master/Doctor | 1-4 | - | No | |
| This course introduces deep learning techniques and theories with an emphasis on practical application. It includes concepts and methods used to optimize highly parameterized models, the modules that make up them, and a common neural network architecture. Applications ranging from computer vision to natural language processing and decision making (reinforcement learning) are demonstrated. Through in-depth programming assignments, students learn how to implement these basic building blocks and build them using PyTorch, a popular deep learning library. In the final project, students will apply what they learn to real-world scenarios by exploring these concepts with problems they are passionate about. | |||||||||
| SFC5025 | Project Research in Smart Facotry Convergence | 3 | 6 | Major | Master | Korean | Yes | ||
| This course must be taken by students who have completed all project-based courses. It is an individual research course in the graduate program that proposes project topics based on industry demand and conducts individual practical research. Students must select a practical project research topic and submit a research plan after receiving confirmation from the project advisor. After conducting in-depth practical project research on the research topic for one semester, the results such as the result report and panel must be submitted, including evaluation of public presentation. | |||||||||
| SFC6000 | Smart Facotry Advanced Capstone Design 1 | 3 | 6 | Major | Doctor | Korean | Yes | ||
| This course draws on the subject of industrial demand-based projects, conducts individual practical research, and acquires research credits for the preparation of doctoral dissertations in the Department of Smart Factory Convergence. Individual working research includes submitting a working project research plan, submitting a report of the results of the research results, and evaluating the presentation. | |||||||||
| SFC6001 | Smart Facotry Advanced Capstone Design 2 | 3 | 6 | Major | Doctor | Korean | Yes | ||
| This course draws on the subject of industrial demand-based projects, conducts individual practical research, and acquires research credits for the preparation of doctoral dissertations in the Department of Smart Factory Convergence. Individual working research includes submitting a working project research plan, submitting a report of the results of the research results, and evaluating the presentation. | |||||||||
| SFC6002 | Doctoral Thesis Research 1 on Smart Factory | 3 | 6 | Major | Doctor | Korean | Yes | ||
| This course is to acquire research credits by individual research for for the preparation of doctoral dissertations in the Department of Smart Factory Convergence. The individual research include technology review, planning of research methology, data acquisition and analysis using experimental and numerical technologies, derivation of conclusion and doctoral thesis writing. | |||||||||