Artificial+Intelligence
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An AI-based, Indoor/Outdoor-Integrated (IOI) GPS System to Bring Seismic Waves in the Terrains of Positioning Technology
KAIST breaks new grounds in positioning technology with an AI-integrated GPS board that works both indoors and out
KAIST (President Kwang Hyung Lee) announced on the 8th that Professor Dong-Soo Han's research team (Intelligent Service Integration Lab) from the School of Computing has developed a GPS system that works both indoors and outdoors with quality precision regardless of the environment.
This Indoor/Outdoor-Integrated GPS System, or IOI GPS System, for short, uses the GPS signals outdoors and estimates locations indoors using signals from multiple sources like an inertial sensor, pressure sensors, geomagnetic sensors, and light sensors. To this end, the research team developed techniques to detect environmental changes such as entering a building, and methods to detect entrances, ground floors, stairs, elevators and levels of buildings by utilizing artificial intelligence techniques. Various landmark detecting techniques were also incorporated with pedestrian dead reckoning (PDR), a navigation tool for pedestrians, to devise the so-called “Sensor-Fusion Positioning Algorithm”.
To date, it was common to estimate locations based on wireless LAN signals or base station signals in a space where the GPS signal could not reach. However, the IOI GPS enables positioning even in buildings without signals nor indoor maps.
The algorithm developed by the research team can provide accurate floor information within a building where even big tech companies like Google and Apple's positioning services do not provide. Unlike other positioning methods that rely on visual data, geomagnetic positioning techniques, or wireless LAN, this system also has the advantage of not requiring any prior preparation. In other words, the foundation to enable the usage of a universal GPS system that works both indoors and outdoors anywhere in the world is now ready.
The research team also produced a circuit board for the purpose of operating the IOI GPS System, mounted with chips to receive and process GPS, Wi-Fi, and Bluetooth signals, along with an inertial sensor, a barometer, a magnetometer, and a light sensor. The sensor-fusion positioning algorithm the lab has developed is also incorporated in the board.
When the accuracy of the IOI GPS board was tested in the N1 building of KAIST’s main campus in Daejeon, it achieved an accuracy of about 95% in floor estimation and an accuracy of about 3 to 6 meters in distance estimation. As for the indoor/outdoor transition, the navigational mode change was completed in about 0.3 seconds. When it was combined with the PDR technique, the estimation accuracy improved further down to a scope of one meter.
The research team is now working on assembling a tag with a built-in positioning board and applying it to location-based docent services for visitors at museums, science centers, and art galleries. The IOI GPS tag can be used for the purpose of tracking children and/or the elderly, and it can also be used to locate people or rescue workers lost in disaster-ridden or hazardous sites. On a different note, the sensor-fusion positioning algorithm and positioning board for vehicles are also under development for the tracking of vehicles entering indoor areas like underground parking lots.
When the IOI GPS board for vehicles is manufactured, the research team will work to collaborate with car manufacturers and car rental companies, and will also develop a sensor-fusion positioning algorithm for smartphones. Telecommunication companies seeking to diversify their programs in the field of location-based services will also be interested in the use the IOI GPS.
Professor Dong-Soo Han of the School of Computing, who leads the research team, said, “This is the first time to develop an indoor/outdoor integrated GPS system that can pinpoint locations in a building where there is no wireless signal or an indoor map, and there are an infinite number of areas it can be applied to. When the integration with the Korea Augmentation Satellite System (KASS) and the Korean GPS (KPS) System that began this year, is finally completed, Korea can become the leader in the field of GPS both indoors and outdoors, and we also have plans to manufacture semi-conductor chips for the IOI GPS System to keep the tech-gap between Korea and the followers.”
He added, "The guidance services at science centers, museums, and art galleries that uses IOI GPS tags can provide a set of data that would be very helpful for analyzing the visitors’ viewing traces. It is an essential piece of information required when the time comes to decide when to organize the next exhibit. We will be working on having it applied to the National Science Museum, first.”
The projects to develop the IOI GPS system and the trace analysis system for science centers were supported through Science, Culture, Exhibits and Services Capability Enhancement Program of the Ministry of Science and ICT.
Profile: Dong-Soo Han, Ph.D.Professorddsshhan@kaist.ac.krhttp://isilab.kaist.ac.kr
Intelligent Service Integration Lab.School of Computing
http://kaist.ac.kr/en/
Korea Advanced Institute of Science and Technology (KAIST)Daejeon, Republic of Korea
2022.07.13 View 13690 -
Neuromorphic Memory Device Simulates Neurons and Synapses
Simultaneous emulation of neuronal and synaptic properties promotes the development of brain-like artificial intelligence
Researchers have reported a nano-sized neuromorphic memory device that emulates neurons and synapses simultaneously in a unit cell, another step toward completing the goal of neuromorphic computing designed to rigorously mimic the human brain with semiconductor devices.
Neuromorphic computing aims to realize artificial intelligence (AI) by mimicking the mechanisms of neurons and synapses that make up the human brain. Inspired by the cognitive functions of the human brain that current computers cannot provide, neuromorphic devices have been widely investigated. However, current Complementary Metal-Oxide Semiconductor (CMOS)-based neuromorphic circuits simply connect artificial neurons and synapses without synergistic interactions, and the concomitant implementation of neurons and synapses still remains a challenge. To address these issues, a research team led by Professor Keon Jae Lee from the Department of Materials Science and Engineering implemented the biological working mechanisms of humans by introducing the neuron-synapse interactions in a single memory cell, rather than the conventional approach of electrically connecting artificial neuronal and synaptic devices.
Similar to commercial graphics cards, the artificial synaptic devices previously studied often used to accelerate parallel computations, which shows clear differences from the operational mechanisms of the human brain. The research team implemented the synergistic interactions between neurons and synapses in the neuromorphic memory device, emulating the mechanisms of the biological neural network. In addition, the developed neuromorphic device can replace complex CMOS neuron circuits with a single device, providing high scalability and cost efficiency.
The human brain consists of a complex network of 100 billion neurons and 100 trillion synapses. The functions and structures of neurons and synapses can flexibly change according to the external stimuli, adapting to the surrounding environment. The research team developed a neuromorphic device in which short-term and long-term memories coexist using volatile and non-volatile memory devices that mimic the characteristics of neurons and synapses, respectively. A threshold switch device is used as volatile memory and phase-change memory is used as a non-volatile device. Two thin-film devices are integrated without intermediate electrodes, implementing the functional adaptability of neurons and synapses in the neuromorphic memory.
Professor Keon Jae Lee explained, "Neurons and synapses interact with each other to establish cognitive functions such as memory and learning, so simulating both is an essential element for brain-inspired artificial intelligence. The developed neuromorphic memory device also mimics the retraining effect that allows quick learning of the forgotten information by implementing a positive feedback effect between neurons and synapses.”
This result entitled “Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse” was published in the May 19, 2022 issue of Nature Communications.
-Publication:Sang Hyun Sung, Tae Jin Kim, Hyera Shin, Tae Hong Im, and Keon Jae Lee (2022) “Simultaneous emulation of synaptic and intrinsic plasticity using a memristive synapse,” Nature Communications May 19, 2022 (DOI: 10.1038/s41467-022-30432-2)
-Profile:Professor Keon Jae Leehttp://fand.kaist.ac.kr
Department of Materials Science and EngineeringKAIST
2022.05.20 View 16228 -
Energy-Efficient AI Hardware Technology Via a Brain-Inspired Stashing System
Researchers demonstrate neuromodulation-inspired stashing system for the energy-efficient learning of a spiking neural network using a self-rectifying memristor array
Researchers have proposed a novel system inspired by the neuromodulation of the brain, referred to as a ‘stashing system,’ that requires less energy consumption. The research group led by Professor Kyung Min Kim from the Department of Materials Science and Engineering has developed a technology that can efficiently handle mathematical operations for artificial intelligence by imitating the continuous changes in the topology of the neural network according to the situation. The human brain changes its neural topology in real time, learning to store or recall memories as needed. The research group presented a new artificial intelligence learning method that directly implements these neural coordination circuit configurations.
Research on artificial intelligence is becoming very active, and the development of artificial intelligence-based electronic devices and product releases are accelerating, especially in the Fourth Industrial Revolution age. To implement artificial intelligence in electronic devices, customized hardware development should also be supported. However most electronic devices for artificial intelligence require high power consumption and highly integrated memory arrays for large-scale tasks. It has been challenging to solve these power consumption and integration limitations, and efforts have been made to find out how the human brain solves problems.
To prove the efficiency of the developed technology, the research group created artificial neural network hardware equipped with a self-rectifying synaptic array and algorithm called a ‘stashing system’ that was developed to conduct artificial intelligence learning. As a result, it was able to reduce energy by 37% within the stashing system without any accuracy degradation. This result proves that emulating the neuromodulation in humans is possible.
Professor Kim said, "In this study, we implemented the learning method of the human brain with only a simple circuit composition and through this we were able to reduce the energy needed by nearly 40 percent.”
This neuromodulation-inspired stashing system that mimics the brain’s neural activity is compatible with existing electronic devices and commercialized semiconductor hardware. It is expected to be used in the design of next-generation semiconductor chips for artificial intelligence.
This study was published in Advanced Functional Materials in March 2022 and supported by KAIST, the National Research Foundation of Korea, the National NanoFab Center, and SK Hynix.
-Publication:
Woon Hyung Cheong, Jae Bum Jeon†, Jae Hyun In, Geunyoung Kim, Hanchan Song, Janho An, Juseong Park, Young Seok Kim, Cheol Seong Hwang, and Kyung Min Kim (2022)
“Demonstration of Neuromodulation-inspired Stashing System for Energy-efficient Learning of Spiking Neural Network using a Self-Rectifying Memristor Array,” Advanced FunctionalMaterials March 31, 2022 (DOI: 10.1002/adfm.202200337)
-Profile:
Professor Kyung Min Kimhttp://semi.kaist.ac.kr https://scholar.google.com/citations?user=BGw8yDYAAAAJ&hl=ko
Department of Materials Science and EngineeringKAIST
2022.05.18 View 13088 -
Professor Lik-Hang Lee Offers Metaverse Course for Hong Kong Productivity Council
Professor Lik-Hang Lee from the Department of Industrial System Engineering will offer a metaverse course in partnership with the Hong Kong Productivity Council (HKPC) from the Spring 2022 semester to Hong Kong-based professionals.
“The Metaverse Course for Professionals” aims to nurture world-class talents of the metaverse in response to surging demand for virtual worlds and virtual-physical blended environments. The HKPC’s R&D scientists, consultants, software engineers, and related professionals will attend the course. They will receive a professional certificate on managing and developing metaverse skills upon the completion of this intensive course.
The course will provide essential skills and knowledge about the parallel virtual universe and how to leverage digitalization and industrialization in the metaverse era. The course includes comprehensive modules, such as designing and implementing virtual-physical blended environments, metaverse technology and ecosystems, immersive smart cities, token economies, and intelligent industrialization in the metaverse era.
Professor Lee believes in the decades to come that we will see rising numbers of virtual worlds in cyberspace known as the ‘Immersive Internet’ that will be characterized by high levels of immersiveness, user interactivity, and user-machine collaborations.
“Consumers in virtual worlds will create novel content as well as personalized products and services, becoming as catalyst for ‘hyperpersonalization’ in the next industrial revolution,” he said.
Professor Lee said he will continue offering world-class education related to the metaverse to students in KAIST and professionals from various industrial sectors, as his Augmented Reality and Media Lab will focus on a variety of metaverse topics such as metaverse campuses and industrial metaverses.
The HKPC has worked to address innovative solutions for Hong Kong industries and enterprises since 1967, helping them achieve optimized resource utilization, effectiveness, and cost reduction as well as enhanced productivity and competitiveness in both local and international markets. The HKPC has advocated for facilitating Hong Kong’s reindustrialization powered by Industry 4.0 and e-commerce 4.0 with a strong emphasis on R&D, IoT, AI, digital manufacturing.
The Augmented Reality and Media Lab led by Professor Lee will continue its close partnerships with HKPC and its other partners to help build the epicentre of the metaverse in the region. Furthermore, the lab will fully leverage its well-established research niches in user-centric, virtual-physical cyberspace (https://www.lhlee.com/projects-8 ) to serve upcoming projects related to industrial metaverses, which aligns with the departmental focus on smart factories and artificial intelligence.
2022.04.06 View 9891 -
KAIST ISPI Releases Report on the Global AI Innovation Landscape
Providing key insights for building a successful AI ecosystem
The KAIST Innovation Strategy and Policy Institute (ISPI) has launched a report on the global innovation landscape of artificial intelligence in collaboration with Clarivate Plc. The report shows that AI has become a key technology and that cross-industry learning is an important AI innovation. It also stresses that the quality of innovation, not volume, is a critical success factor in technological competitiveness.
Key findings of the report include:
• Neural networks and machine learning have been unrivaled in terms of scale and growth (more than 46%), and most other AI technologies show a growth rate of more than 20%.
• Although Mainland China has shown the highest growth rate in terms of AI inventions, the influence of Chinese AI is relatively low. In contrast, the United States holds a leading position in AI-related inventions in terms of both quantity and influence.
• The U.S. and Canada have built an industry-oriented AI technology development ecosystem through organic cooperation with both academia and the Government. Mainland China and South Korea, by contrast, have a government-driven AI technology development ecosystem with relatively low qualitative outputs from the sector.
• The U.S., the U.K., and Canada have a relatively high proportion of inventions in robotics and autonomous control, whereas in Mainland China and South Korea, machine learning and neural networks are making progress. Each country/region produces high-quality inventions in their predominant AI fields, while the U.S. has produced high-impact inventions in almost all AI fields.
“The driving forces in building a sustainable AI innovation ecosystem are important national strategies. A country’s future AI capabilities will be determined by how quickly and robustly it develops its own AI ecosystem and how well it transforms the existing industry with AI technologies. Countries that build a successful AI ecosystem have the potential to accelerate growth while absorbing the AI capabilities of other countries. AI talents are already moving to countries with excellent AI ecosystems,” said Director of the ISPI Wonjoon Kim.
“AI, together with other high-tech IT technologies including big data and the Internet of Things are accelerating the digital transformation by leading an intelligent hyper-connected society and enabling the convergence of technology and business. With the rapid growth of AI innovation, AI applications are also expanding in various ways across industries and in our lives,” added Justin Kim, Special Advisor at the ISPI and a co-author of the report.
2021.12.21 View 10093 -
Prof. Sang Wan Lee Selected for 2021 IBM Academic Award
Professor Sang Wan Lee from the Department of Bio and Brain Engineering was selected as the recipient of the 2021 IBM Global University Program Academic Award. The award recognizes individual faculty members whose emerging science and technology contains significant interest for universities and IBM.
Professor Lee, whose research focuses on artificial intelligence and computational neuroscience, won the award for his research proposal titled A Neuroscience-Inspired Approach for Metacognitive Reinforcement Learning. IBM provides a gift of $40,000 to the recipient’s institution in recognition of the selection of the project but not as a contract for services.
Professor Lee’s project aims to exploit the unique characteristics of human reinforcement learning. Specifically, he plans to examines the hypothesis that metacognition, a human’s ability to estimate their uncertainty level, serves to guide sample-efficient and near-optimal exploration, making it possible to achieve an optimal balance between model-based and model-free reinforcement learning.
He was also selected as the winner of the Google Research Award in 2016 and has been working with DeepMind and University College London to conduct basic research on decision-making brain science to establish a theory on frontal lobe meta-enhance learning.
"We plan to conduct joint research for utilizing brain-based artificial intelligence technology and frontal lobe meta-enhanced learning technology modeling in collaboration with an international research team including IBM, DeepMind, MIT, and Oxford,” Professor Lee said.
2021.06.25 View 14913 -
Professor Alice Haeyun Oh to Join GPAI Expert Group
Professor Alice Haeyun Oh will participate in the Global Partnership on Artificial Intelligence (GPAI), an international and multi-stakeholder initiative hosted by the OECD to guide the responsible development and use of AI. In collaboration with partners and international organizations, GPAI will bring together leading experts from industry, civil society, government, and academia.
The Korean Ministry of Science and ICT (MSIT) officially announced that South Korea will take part in GPAI as one of the 15 founding members that include Canada, France, Japan, and the United States.
Professor Oh has been appointed as a new member of the Responsible AI Committee, one of the four committees that GPAI established along with the Data Governance Committee, Future of Work Committee, and Innovation and Commercialization Committee.
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2020.06.22 View 9780 -
Professor Jong Chul Ye Appointed as Distinguished Lecturer of IEEE EMBS
Professor Jong Chul Ye from the Department of Bio and Brain Engineering was appointed as a distinguished lecturer by the International Association of Electrical and Electronic Engineers (IEEE) Engineering in Medicine and Biology Society (EMBS). Professor Ye was invited to deliver a lecture on his leading research on artificial intelligence (AI) technology in medical video restoration. He will serve a term of two years beginning in 2020.
IEEE EMBS's distinguished lecturer program is designed to educate researchers around the world on the latest trends and technology in biomedical engineering. Sponsored by IEEE, its members can attend lectures on the distinguished professor's research subject.
Professor Ye said, "We are at a time where the importance of AI in medical imaging is increasing.” He added, “I am proud to be appointed as a distinguished lecturer of the IEEE EMBS in recognition of my contributions to this field.”
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2020.02.27 View 13632 -
Professor Minsoo Rhu Recognized as Facebook Research Scholar
Professor Minsoo Rhu from the School of Electrical Engineering was selected as the recipient of the Systems for Machine Learning Research Awards presented by Facebook.
Facebook launched the award last year with the goal of funding impactful solutions in the areas of developer tookits, compilers and code generation, system architecture, memory technologies, and machine learning accelerator support.
A total of 167 scholars from 100 universities representing 26 countries submitted research proposals, and Facebook selected final 10 scholars. Professor Rhu made the list with his research topic ‘A Near-Memory Processing Architecture for Training Recommendation Systems.’ He will receive 5,000 USD in research funds at the award ceremony which will take place during this year’s AI Systems Faculty Summit at the Facebook headquarters in Menlo Park, California.
Professor Rhu’s submission was based on research on ‘Memory-Centric Deep Learning System Architecture’ that he carried out for three years under the auspices of Samsung Science and Technology Foundation from 2017. It was an academic-industrial cooperation research project in which leading domestic companies like Samsung Electronics and SK Hynix collaborated to make a foray into the global memory-centric smart system semiconductor market.
Professor Rhu who joined KAIST in 2018 has led various systems research projects to accelerate the AI computing technology while working at NVIDIA headquarters from 2014.
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2020.02.21 View 12007 -
New IEEE Fellow, Professor Jong Chul Ye
Professor Jong Chul Ye from the Department of Bio and Brain Engineering was named a new fellow of the Institute of Electrical and Electronics Engineers (IEEE). IEEE announced this on December 1 in recognition of Professor Ye’s contributions to the development of signal processing and artificial intelligence (AI) technology in the field of biomedical imaging.
As the world’s largest society in the electrical and electronics field, IEEE names the top 0.1% of their members as fellows based on their research achievements.Professor Ye has published more than 100 research papers in world-leading journals in the biomedical imaging field, including those affiliated with IEEE.
He also gave a keynote talk at the yearly conference of the International Society for Magnetic Resonance Imaging (ISMRM) on medical AI technology. In addition, Professor Ye has been appointed to serve as the next chair of the Computational Imaging Technical Committee of the IEEE Signal Processing Society, and the chair of the IEEE Symposium on Biomedical Imaging (ISBI) 2020 to be held in April in Iowa, USA.
Professor Ye said, “The importance of AI technology is developing in the biomedical imaging field. I feel proud that my contributions have been internationally recognized and allowed me to be named an IEEE fellow.”
2019.12.18 View 12392 -
KAIST and Google Jointly Develop AI Curricula
KAIST selected the two professors who will develop AI curriculum under the auspices of the KAIST-Google Partnership for AI Education and Research. The Graduate School of AI announced the two authors among the 20 applicants who will develop the curriculum next year. They will be provided 7,500 USD per subject.
Professor Changho Suh from the School of Electrical Engineering and Professor Yong-Jin Yoon from the Department of Mechanical Engineering will use Google technology such as TensorFlow, Google Cloud, and Android to create the curriculum.
Professor Suh’s “TensorFlow for Information Theory and Convex Optimization “will be used for curriculum in the graduate courses and Professor Yoon’s “AI Convergence Project Based Learning (PBL)” will be used for online courses. Professor Yoon’s course will explore and define problems by utilizing AI and experiencing the process of developing products that use AI through design thinking, which involves product design, production, and verification. Professor Suh’s course will discus“information theory and convergence,” which uses basic sciences and engineering as well as AI, machine learning, and deep learning.
2019.12.04 View 17345 -
AI to Determine When to Intervene with Your Driving
(Professor Uichin Lee (left) and PhD candidate Auk Kim)
Can your AI agent judge when to talk to you while you are driving? According to a KAIST research team, their in-vehicle conservation service technology will judge when it is appropriate to contact you to ensure your safety.
Professor Uichin Lee from the Department of Industrial and Systems Engineering at KAIST and his research team have developed AI technology that automatically detects safe moments for AI agents to provide conversation services to drivers.
Their research focuses on solving the potential problems of distraction created by in-vehicle conversation services. If an AI agent talks to a driver at an inopportune moment, such as while making a turn, a car accident will be more likely to occur.
In-vehicle conversation services need to be convenient as well as safe. However, the cognitive burden of multitasking negatively influences the quality of the service. Users tend to be more distracted during certain traffic conditions. To address this long-standing challenge of the in-vehicle conversation services, the team introduced a composite cognitive model that considers both safe driving and auditory-verbal service performance and used a machine-learning model for all collected data.
The combination of these individual measures is able to determine the appropriate moments for conversation and most appropriate types of conversational services. For instance, in the case of delivering simple-context information, such as a weather forecast, driver safety alone would be the most appropriate consideration. Meanwhile, when delivering information that requires a driver response, such as a “Yes” or “No,” the combination of driver safety and auditory-verbal performance should be considered.
The research team developed a prototype of an in-vehicle conversation service based on a navigation app that can be used in real driving environments. The app was also connected to the vehicle to collect in-vehicle OBD-II/CAN data, such as the steering wheel angle and brake pedal position, and mobility and environmental data such as the distance between successive cars and traffic flow.
Using pseudo-conversation services, the research team collected a real-world driving dataset consisting of 1,388 interactions and sensor data from 29 drivers who interacted with AI conversational agents. Machine learning analysis based on the dataset demonstrated that the opportune moments for driver interruption could be correctly inferred with 87% accuracy.
The safety enhancement technology developed by the team is expected to minimize driver distractions caused by in-vehicle conversation services. This technology can be directly applied to current in-vehicle systems that provide conversation services. It can also be extended and applied to the real-time detection of driver distraction problems caused by the use of a smartphone while driving.
Professor Lee said, “In the near future, cars will proactively deliver various in-vehicle conversation services. This technology will certainly help vehicles interact with their drivers safely as it can fairly accurately determine when to provide conversation services using only basic sensor data generated by cars.”
The researchers presented their findings at the ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp’19) in London, UK. This research was supported in part by Hyundai NGV and by the Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.
(Figure: Visual description of safe enhancement technology for in-vehicle conversation services)
2019.11.13 View 20561