• Journal of the Society of Disaster Information
• /
• v.17 no.1
• /
• pp.176-183
• /
• 2021

Purpose: The introduction of smart factories that reflect the 4th industrial revolution technologies such as AI, IoT, and VR, has been actively promoted in Korea. However, in order to solve various problems arising from existing file-based operating systems, this research will focus on identifying and verifying non-file system-based data protection technology. Method: The research will measure security storage that cannot be identified or controlled by the operating system. How to activate secure storage based on the input of digital key values. Establish a control unit that provides input and output information based on BIOS activation. Observe non-file-type structure so that mapping behavior using second meta-data can be performed according to the activation of the secure storage. Result: First, the creation of non-file system-based secure storage's data input/output were found to match the hash function value of the sample data with the hash function value of the normal storage and data. Second, the data protection performance experiments in secure storage were compared to the hash function value of the original file with the hash function value of the secure storage after ransomware activity to verify data protection performance against malicious ransomware. Conclusion: Smart factory technology is a nationally promoted technology that is being introduced to the public and this research implemented and experimented on a new concept of data protection technology to protect crucial data within the information system. In order to protect sensitive data, implementation of non-file-type secure storage technology that is non-dependent on file system is highly recommended. This research has proven the security and safety of such technology and verified its purpose.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.81-83
• /
• 2021

4차 산업 혁명과 함께 인공지능이 발전 하고 있다. 그 CNN 등 과 같은 이미지 관련 신경망들이 발전되어 가스 탐지와 같은 여러 분야에서 사용되고 있다. 하지만 가스 탐지는 Box 형태의 탐지가 일반적이고 Segmentation에 관한 연구는 있지만 연기와 같이 경계선이 불분명한 개체에 대해서는 연구가 미비하다. 본 논문에서는 Segmentation에 강력한 성능을 보이는 U-net을 활용하여 Box 형태가 아닌 Segmentation을 진행하여 픽셀단위로 연기를 탐지하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.187-189
• /
• 2023

IoT(Internet of Things), 빅데이터, AI(Artificial Intelligence), 클라우드와 같은 ICT(Information and Communications Technology) 기술이 발전함에 따라 ICT와 제조기술이 융합된 스마트팩토리가 발전하고 있다. 이는 2개의 영역과 5개의 계층으로 구성되어 기타 환경들과 상이한 구조를 가지고 있으며, 각 영역·계층별 발생 가능한 보안위협도 상이하다. 또한, 각 영역과 계층이 연결됨에 따라 발생 가능한 보안위협이 증가하고 있으며, 이에 대한 효율적인 대응을 위하여 스마트팩토리 영역·계층별 환경을 고려한 대응체계 마련이 필요한 실정이다. 따라서, 본 논문에서는 스마트팩토리 영역·계층별 발생 가능한 보안위협을 분석하고, 이에 대응하기 위한 대응체계 도출 기법을 제안한다.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.4
• /
• pp.165-172
• /
• 2020

In this paper we propose an uses on-device-based edge computing technology and big data analysis methods through the use of on-device-based edge computing technology and analysis of big data, which are distributed computing paradigms that introduce computations and storage devices where necessary to solve problems such as transmission delays that occur when data is transmitted to central centers and processed in current general smart factories. However, even if edge computing-based technology is applied in practice, the increase in devices on the network edge will result in large amounts of data being transferred to the data center, resulting in the network band reaching its limits, which, despite the improvement of network technology, does not guarantee acceptable transfer speeds and response times, which are critical requirements for many applications. It provides the basis for developing into an AI-based facility prediction conservation analysis tool that can apply deep learning suitable for big data in the future by supporting intelligent facility management that can support productivity growth through research that can be applied to the field of facility preservation and smart factory industry with integrated hardware technology that can accommodate these requirements and factory management and control technology.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.4
• /
• pp.15-31
• /
• 2023

In this study, we propose a novel approach to analyze big data related to patents in the field of smart factories, utilizing the Latent Dirichlet Allocation (LDA) topic modeling method and the generative artificial intelligence technology, ChatGPT. Our method includes extracting valuable insights from a large data-set of associated patents using LDA to identify latent topics and their corresponding patent documents. Additionally, we validate the suitability of the topics generated using generative AI technology and review the results with domain experts. We also employ the powerful big data analysis tool, KNIME, to preprocess and visualize the patent data, facilitating a better understanding of the global patent landscape and enabling a comparative analysis with the domestic patent environment. In order to explore quantitative and qualitative comparative advantages at this juncture, we have selected six indicators for conducting a quantitative analysis. Consequently, our approach allows us to explore the distinctive characteristics and investment directions of individual countries in the context of research and development and commercialization, based on a global-scale patent analysis in the field of smart factories. We anticipate that our findings, based on the analysis of global patent data in the field of smart factories, will serve as vital guidance for determining individual countries' directions in research and development investment. Furthermore, we propose a novel utilization of GhatGPT as a tool for validating the suitability of selected topics for policy makers who must choose topics across various scientific and technological domains.

• Communications of Mathematical Education
• /
• v.34 no.1
• /
• pp.1-15
• /
• 2020

Today's healthcare, intelligent robots, smart home systems, and car sharing are already innovating with cutting-edge information and communication technologies such as Artificial Intelligence (AI), the Internet of Things, the Internet of Intelligent Things, and Big data. It is deeply affecting our lives. In the factory, robots have been working for humans more than several decades (FA, OA), AI doctors are also working in hospitals (Dr. Watson), AI speakers (Giga Genie) and AI assistants (Siri, Bixby, Google Assistant) are working to improve Natural Language Process. Now, in order to understand AI, knowledge of mathematics becomes essential, not a choice. Thus, mathematicians have been given a role in explaining such mathematics that make these things possible behind AI. Therefore, the authors wrote a textbook 'Basic Mathematics for Artificial Intelligence' by arranging the mathematics concepts and tools needed to understand AI and machine learning in one or two semesters, and organized lectures for undergraduate and graduate students of various majors to explore careers in artificial intelligence. In this paper, we share our experience of conducting this class with the full contents in http://matrix.skku.ac.kr/math4ai/.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.6
• /
• pp.203-212
• /
• 2019

Along with the recent fourth industrial revolution, the world's manufacturing powerhouses are pushing for national strategies to revive the sluggish manufacturing industry. Moon Jae-in, the government is in accordance with the trend, called 'advancement of science and technology is leading the fourth round of the Industrial Revolution' strategy. Intelligent information technology such as IoT, Cloud, Big Data, Mobile, and AI, which are key technologies that lead the fourth industrial revolution, is promoting the emergence of new industries such as robots and 3D printing and the smarting of existing major manufacturing industries. Advances in technologies such as smart factories have enabled IoT-based sensing technology to measure various data that could not be collected before, and data generated by each process has also exploded. Thus, this paper uses data generators to generate virtual data that can occur in smart factories, and uses them to analyze the cause of the defect in real time and to predict the replacement cycle of the facility.

• Industry Promotion Research
• /
• v.5 no.4
• /
• pp.1-13
• /
• 2020

Since the 4th industrial revolution was thrown into the world at the Davos World Economic Forum in January 2016, the world has been undergoing major social and economic changes. In this study, the direction of quality management in the 4th industrial revolution era was examined. First, in all the major countries the industrial structural changes and smart business models were confirmed due to the convergence of new ICT such as IoT, robotics, 3D printing, big data, and AI with the existing technologies and industries. Second, we found that although the core technology level of the 4th industrial revolution in Korea is not as good as that of advanced countries, we have been working on expanding smart production methods and creating new industries by utilizing new ICT. Finally, it was confirmed that quality management is a real-time implementation of new ICT that reflects the needs of the market in real time based on big data from the planning and design stage of products or services.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.17 no.4
• /
• pp.63-75
• /
• 2021

It is necessary to respond to rapid technological changes such as the 4th industrial revolution and digital transformation across industries. And, a change in the university education system is necessary in a crisis situation of universities due to the rapid decrease of the school-age population. This study is aimed at practical competency with the ability to apply intelligent information technology to their job fields based on a basic understanding of intelligent information technology rather than advanced competency centered on theory and research. Instead of presenting the curriculum system diagram so that it can be flexibly applied to the design and development of the curriculum for intelligent information technology, training modules according to job and level were presented. In relation to intelligent information technology, a questionnaire was conducted for college professors, and industry experts were conducted on the derived educational modules to reflect the opinions of the industry. Industry experts said that collaboration with PBL, Capstone, and industry is necessary to improve problem-solving and communication skills.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.2
• /
• pp.173-178
• /
• 2019

Recently, the types and amount of data generated, collected, and measured in IoT such as smart home, security, and factory are increasing. The technologies for IoT service include sensor devices to measure desired data, embedded software to control the devices such as signal processing, wireless network protocol to transmit and receive the measured data, and big data and AI-based analysis. In this paper, we focused on developing a gateway for interfacing heterogeneous sensor network protocols that are used in various IoT devices and propose a heterogeneous network interface IoT gateway. We utilized a OpenWrt-based wireless routers and used 6LoWAN stack for IP-based communication via BLE and IEEE 802.15.4 adapters. We developed a software to convert Z-Wave and LoRa packets into IP packet using our Python-based middleware. We expect the IoT gateway to be used as an effective device for collecting IoT big data.