|OS01||Artificial Intelligence and Robot for Human
Jong-Wook Kim, Dong-A University, Korea
AI and robot technologies are currently rapidly developing, and the side effects are also seriously concerned. This session introduces what AI and robots need for humans to lead a comfortable and happy life, and what technologies and products are being developed accordingly. A wheelchair robot capable of autonomous driving and ethical dilemmas that elderly care robots may face are introduced. In addition, technology related to the explainability of AI and technology for recognizing human motion at home will also be introduced.
|OS02||Big Data Analysis for Intellectual Property R&D (IP R&D)
Sunghae Jun, Cheongju University, Korea
We propose big data analysis for IP-R&D in this session. Big data analysis refers to technology that utilizes large amount of structured or unstructured data sets for extracting its unique value and analyzing the results. This state-of-the-art technology can be applied to IP-R&D analysis. IP-R&D is a technology strategy focused on intellectual property. The concept of IP-R&D is defined as analyzing intellectual property portfolio before carrying out R&D, so as to dominate the target market in advance. Intellectual property is an excellent source for big data analysis since it contains a great number of data about technology, trademark, design, copyright, and so on. Particularly, patent has distinct features, which are objectivity, novelty, creativity, and industrial applicability. Since patent system aims to protect inventors, each patent has general bibliographic data and its own technical information. With the development of Internet, it is possible to access to a large amount of patent data easily through web sites. For these reasons, big data analysis of patent is very important for management of technology, especially forecasting, valuation, and research trends of technology. Existing methods of patent analysis, such as Delphi and technology road-mapping, have been depended on expert’s prior knowledge, so the results of the analysis were subjective and unstable. However, it is possible to utilize patent big data with objective methodologies nowadays since the data can be easily accessible. In this session, we discuss the objective methodologies of patent big data analysis, such as advanced statistical methods, data mining, text mining, artificial intelligence, machine learning, and so forth. Using these methods and techniques, we develop expert systems for patent big data analysis with many researchers’ objective models in their research papers published in this session. In addition it is expected that patent data can be analyzed more efficiently with big data science methodologies. So we also propose various types of methods to establish technology strategies for forecasting brand-new, emerging, and promising technologies in this session.
|OS03||Continual Learning and Emergence of Intelligent Systems
Yuichiro Toda, Okayama University, Japan
Naoki Masuyama, Osaka Prefecture University, Japan
Seiki Ubukata, Osaka Prefecture University Japan
Wei Hong Chin, Tokyo Metropolitan University, Japan
The purpose of this special session is to discuss the continual learning and the emergence of intelligent systems for extracting, storing and exploiting the multidimensional information from the environmental data with multiple modalities. This session welcomes fundamental research on the information extraction techniques and knowledge base relation learning approaches. In addition, this session also welcomes research on the applied topic about the exploiting knowledge with intelligent applications/robots.
|OS04||Cyber Physical System, Chaotic Dynamic and Deep Learning in Industrial and Emotion Application
Youngchul Bae, Chonnam National University, Korea
Cyper physical system, chaotic dynamics and deep learning have interesting in many fields inclusing industraial and emotion area. Thus, in this organization session, we focus on their theory and application. This OS is expected to be a session that can contribute to the theory and application for related sciecne.
|OS05||Fuzzy Modeling and Its Applications
Jin Hee Yoon, Sejong University, Korea
In this session, we introduce some new fuzzy statistical models and applications their applications to the financial data, electricity consumption data, and human factor data.
|OS06||Intelligent Systems for Robotics and Vehicles
Young-Jae Ryoo, Mokpo National University, Korea
Recently, artificial intelligence is one of the hottest research area all over the world. Many robotic systems insist on having intelligence, attributed to the reasoning and decision making of human beings. Advanced intelligent system includes fuzzy logic, artificial neural networks, machine learning, deep learning and so on. This special session focuses on the intelligent system and its application for robotics. Its areas reach out comprehensive ranges. The papers presented in this organized session will be recommended to submit for the special issue on advanced intelligent system for robotics.
|OS07||Technical Application of Automation in Maritime Domain
Joo-Sung Kim, Mokpo National Maritime University, Korea
In the maritime domain, technological research on the intelligent navigation system is rapidly progressing along with the introduction of the MASS (Maritime Autonomous Surface Ship). The intelligent navigation system is being applied in maritime industries based on the automated navigation system as well as decision support for the safe navigation of human officers. Data generated from merchant ships, harbors, logistics chains, and marine environments are becoming valuable with the development of computer science and marine Information Technology. The data generated in the vessels’ navigation can provide valuable information to predict the movement and condition of the vessels and prevent potential marine accidents. In this Organized Session (OS), we propose the automated systems for preventing ship collision and optimized safe routes, including the research results of automated systems for detecting anomalous behaviors of vessels. These include the following research findings: 1) Decision-making support method for Vessel Traffic Services (VTS) operators to manage ship traffic conditions in VTS areas, and the navigation risk prediction tool to prevent potential marine accidents through the concept of ship surveillance priority index. 2) Determination method of collision risk among vessels anchored and in navigation within the designated anchorage. Validation analysis of the proposed model and methods of predicting collision risk in terms of VTSO. 3) Local route planning algorithm that takes collision-avoidance actions in compliance with COLREGs rules by using a fuzzy inference system based on near-collision (FIS-NC), ship domain (SD), and velocity obstacle (VO).
|OS08||Intelligent Systems using Biosignal and Biopotential
Han Ul Yoon, Yonsei University (Mirae Campus), Korea
This organized session aims at discussing various biosignal/biopotential-based intelligent system applications and related topics. Specifically, we would like to share both theoretical and practical approaches to
|OS09||Digital Twin for Smart River Management
Ho Hyun Lee, K-water, Korea
Data-based safety measures are required due to the aging of large-scale SOC dams, increasing natural disasters, and flood damage caused by climate change. It is necessary to make quick and accurate decisions for river management through an understanding of existing civil and environment-based hardware structures and improving the accuracy of physical and chemical modeling, which has been reviewed for a long time and changed slowly. To renovate this situation drastically, the existing hydraulic model and ICT-based data model should be established to operate dams and rivers safely. K-water is presently promoting the “Seomjin Digital Twin Project”, which is sponsored by NIA(National Information Society Agency) and wants to share the results of smart river management.
Kwangil Lee, Korea Maritime and Ocean University, Korea
In the 4th Industrial Revolution era, a new concept of shipping logistics 4.0 (Shipping 4.0) was introduced that combines advanced technologies such as artificial intelligence, big data, and the Internet of Things throughout the marine industry. In maritime logistics 4.0, the smart and autonomous ship technology has been spotlighted can automatically recognize, analyze maritime conditions and perform work automatically, reducing accidents due to human error at sea and improving efficiency and safety. This session will be focused on the smart and autonomous ship technology including situational awareness, collision avoidance, autonomous/intelligent control, digital interface, maritime cyber security, safety, maritime connectivity, big data analysis and data harmonization but not limited to. We also address the related works for the seamless operation with smart/autonomous ship and smart port.
|OS11||Intelligent Systems and Fuzzy Modeling
Jin-Tsong Jeng, National Formosa University, Taiwan
Shun-Feng Su, National Taiwan University of Science and Technology, Taiwan
In this special session, we intend to collect papers that reflect current progress in the Intelligent Systems and Fuzzy Modeling. We hope thoese papers can set a milestone for the Intelligent Syetms and Fuzzy Modeling and also provide ideas for further exploration in this promising research area. At present, there are five papers in this topic in our collected papers.
|OS12||Maritime PNT(Positioning, Navigation, and Timing) and GIS
Sang Hyun Park, KRISO, Korea
In the proposed session, we would like to introduce Korea’s major research in the field of PNT and GIS, which are major sectors of AtoN for maritime safety. The proposed session consists of two papers on maritime GIS and smart AtoN and the other papers on radio navigation systems. Through papers to be published in the proposed session, we would like to share research results and promote maritime safety around the world.
|OS13||AI, SW quality and test automation design
Jung-Sook Kim, Kimpo University, Korea
A study on quality assurance methods for AI used in various industries. In addition, SW test automation design techniques and research cases in various environments that are spotlighted in non-face-to-face environments such as web UI, Android mobile resource utilization, and E-commerce service operation.
|OS14||Perception in Self-Driving Vehicles
Gon-Woo Kim, Chungbuk National University, Korea
The perception in self-driving vehicles has been performed by using a combination of multiple sensors combined with some state-of-the-art algorithms. Through perception, it can comprehend the environment around the vehicle in real-time. For autonomy and safety, the perception process is crucial because the processed data from the sensory information is used for the decision-making of the autonomous navigation of self-driving vehicles. The combination of various sensors such as cameras, radars, LiDARs have been used for the accurate perception around vehicle. The data acquired from various sensors should be properly processed and fused by the efficient algorithms. In fact, the perception process includes the whole processes as mentioned above. This session aims to share the state-of-the-art research trend, topics and results on perception in self-driving vehicles. It includes papers regarding sensor fusion, deep learning based perception, visual perception and odometry for self-driving vehicles, etc.
|OS15||Intelligent Robotic Systems
Sungshin Kim, Pusan National University, Korea
The object of this session is to present intelligent systems and applications for robotics and discuss how it applies to the real world. Robotics is a fusion of multidisciplinary technologies. There are various research fields such as manipulators, sensing technologies including vision sensor, mobile robots, and so on. Robotic research field is continuously being studied and developed to improve the quality of life. Recently, research on robotics have been actively conducted as the interest in factory automation according to the 4th industrial revolution. A manipulator is a robot widely used in the industrial fields. Among manipulator-related technologies, grasping technology is an indispensable field in manipulator research. For grasping an object, the role of the gripper attached to the end of the robot arm is important. Therefore, the research on a gripper to grasp accurately an object is essential. In addition, there are numerous studies for manipulator control system. Also, intelligent systems have been extensively researched with the development of deep learning in these days. Intelligent systems are being applied in various fields including robotics. There are many studies of applying deep learning to robot vision systems in a variety of ways since deep learning research is being actively conducted in the image processing field. The image data acquired with a vision sensor can contain a lot of information. Therefore, the utilization of vision systems is highly increased in the field of robotics including object detection and recognition, position estimation, inspection monitoring systems, and so on. Deep learning algorithms have the advantage of extracting features automatically. Based on this advantage, it is essential to develop an algorithm that can process image data accurately and efficiently to achieve the purpose. Since robots are continuously being researched and developed to interact with humans, research on how to identify humans, the control system for collaboration, and safety are being conducted.
|OS16||High performance knowledge system and its application to intelligent system for the elderly
Jin-Woo Jung, Dongguk University, Korea
This organized session aims at discussing the basic principles and methods of designing care systems for the elderly based on High performance knowledge system. Topics of interest include, but are not limited to theory and application of:
|OS17||Artificial Intelligence in Metaverse and SCADA
Jung-Sook Kim, Kimpo University, Korea
|OS18||Communications and Signal Processing for Intelligent Robot
Suk-Seung Hwang, Chosun University, Korea
|OS19||Biomedical Signal Analysis using Machine Learning and Deep Learning Approaches
Do-Won Kim, Chonnam National University, Korea
Since the first recording of biomedical signals, scientists have found various signal processing methods to quantify and interpret the data. Various signal-processing methods have uncovered information hidden in the signals, and this knowledge changed the way we approach, diagnose, and treat different diseases. Biomedical signals, however, are often hard to analyze because they are often unbalanced and nonstationary. Moreover, the condition and status of the individual, for instance, age, gender, medical conditions, or even mental status such as drowsiness, affects the signal, thus makes it more challenging to interpret the data. Therefore, statistical interpretation of the data is one of the most important fields in biomedical signal analysis. Machine learning help us to cluster and categorize the data to better understand the important characteristics of the data. Recent advances in deep learning now provide more insight into the data without prior assumptions of the distribution or without preprocessing methods. In this organized session, we promote the speakers and audiences to introduce recent machine learning and deep learning techniques and share their experiences that are actively using in biomedical signal analysis.
|OS20||Human Symbiotic Systems
Tsuyoshi Nakamura, Chubu University, Korea
This special session aims at discussing the basic principles and methods of designing intelligent interaction with the bidirectional communication based on the effective collaboration and symbiosis between the human and the artifact, i.e. robots, agents, computer and so on. We aims at encouraging the academic and industrial discussion about the research on Human-Agent Interaction (HAI), Human-Robot Interaction (HRI), and Human-Computer Interaction (HCI) concerning Symbiotic Systems. Reflecting the fact that this society covers a wide range of topics, in this session we invite the related researchers from a variety of fields including intelligent robotics, human-machine interface, Kansei engineering and so on. Topics of interest include, but are not limited to theory and application of:
Human-Agent Interaction (HAI)
Human-Robot Interaction (HRI)
Human-Computer Interaction (HCI)
Social Communication or Interaction
Partner or Communication Robots
Human Interface Systems
|OS21||AI & Robotics based on agricultural big data in smart farm
Inhoon Jang, Hankyong National University, Korea
Data, AI and robotic solutions have become key elements of modern agriculture to maximize productivity and promote sustainable agriculture of the future. In particular, advances in AI and robotics have enabled smart farms that use less labor.
This trend has recently led researchers to focus their research on artificial intelligence and robotics based on big and long-term data.
This session deals with greenhouse complex environment monitoring and control automation technology, harvest robot manipulation strategy with the help of deep learning image recognition technology, and digital farm implementation technology.
Myung Geun Chun, Chungbuk National University, Korea
This session deals with various aspects of biometric application.
The topic covers the biometric authentication for human and pet animals. On the other hands, for the secure application of biometrics, this session presents a private key generation scheme using the multi modal biometrics and also scheme of splitting the biometric information for the financial sector.
|OS23||Application of Intelligent Mobile Robots
Yong-Tae Kim, Hankyong National University, Korea
Currently, intelligent mobile robots are being studied in various fields due to the development of the 4th industrial revolution, ICT, and artificial intelligence technologies. Mobile robots have become more commonplace in commercial and industrial, military and security applications. Hospitals have been using autonomous mobile robots to move materials for many years. Warehouses have installed mobile robotic systems to efficiently move materials from stocking shelves to order fulfillment zones. Autonomous mobile robot is also a major focus of current research and almost every major university has one or more labs that focus on mobile robot research. The requirements of a mobile robot could be dead reckoning, tactile and proximity sensing, triangulation ranging, collision avoidance, position location, SLAM, navigation and other specific task functions. This special session consists of research results on the application of intelligent mobile robots and aims to identify trends in the latest technologies of intelligent mobile robots in various fields.
|OS24||Applications to Deep Learning
Byung-Jae Choi, Daegu University, Korea
As a session on the application of DNN-based deep learning, we plan to present papers in the field of prediction based on massive data such as prediction of daily cases of COVID-19.
|OS25||Deep learning and intelligent systems
Jee-Hyong Lee, Sungkyunkwan University, Korea
The focus of this session is on the application of novel deep learning techniques to the intelligent systems. Researchers and practitioners will present their recent research and get a chance to keep in touch with problems, open issues and future directions in the field of development of devoted applications using machine learning techniques.
|OS26||Intelligent Robot & Human Symbiotic Systems
Jung Sik Jeong, Mokpo National Maritime University, Korea
|OS27||Spiking Neural Networks and Edge Computing
Keon Myung Lee, Chungbuk National University, Korea