기계공학과

2026년 07월 27일 월요일

UNIST Provides One-Stop Support for Promising Tech Startups

UNIST brought together promising technology startups and venture capital (VC) investors at the 2nd UNI-Venture Challenge on July 22, reinforcing the university's commitment to helping research-based ventures move from innovation to commercialization. The investment pitch competition featured 13 early-stage startups from the Ulsan region, providing founders with opportunities to present their technologies, receive expert feedback, and build relationships with potential investors. More than a pitch competition, the event reflects UNIST's broader approach to entrepreneurship support. Through startup education, mentoring, commercialization assistance, and investor engagement, the university is building an ecosystem that helps technology ventures develop beyond the research stage and prepare for sustainable growth. That effort will enter its next phase in August, when UNIST's Office of University-Industry Cooperation is elevated to the UNIST Venture Foundry. Building on existing entrepreneurship programs, the Venture Foundry will expand support for identifying promising technologies, advancing commercialization, and connecting startups with investors across the Busan-Ulsan-Gyeongnam region. This year's finalists were selected from 28 teams participating in UNIST's entrepreneurship support initiatives. Before the competition, each team received individualized mentoring and pitch coaching to refine its business model and investment presentation. A panel of venture capital professionals evaluated the finalists on technological innovation, market potential, and investment readiness. ANVIX Lab Co., Ltd., led by Professor Hoon Eui Jeong of Department of Mechanical Engineering at UNIST, received the Grand Prize. The Excellence Award went to ELBA Corp., led by CEO Juwon Gu, while Synthkey-Navi, led by Seohyun Choi, received the Merit Award. The program also featured a keynote address by Tae-Gyu Han, Director of UNIST Technology Holdings, on investment trends and startup growth strategies. Throughout the day, participating companies showcased prototypes and met individually with investors to discuss commercialization pathways, investment opportunities, and potential business partnerships. “The UNI-VENTURE CHALLENGE is designed to help promising technologies take the next step toward commercialization by connecting researchers, entrepreneurs, and investors,” said Youngsik Kim, Director of the Office of University-Industry Cooperation. "As the UNIST Venture Foundry expands our entrepreneurship programs across Southeast Korea, we hope to create more opportunities for innovative startups to grow and bring their technologies to market."

2026년 07월 22일 수요일

New Wrinkle Architecture Broadens Optical Responses for Advanced Security Films

Abstract Wrinkle-based diffraction gratings offer a scalable route to pigment-free structural coloration, yet most platforms are limited by globally aligned, linear wrinkles that provide limited spatial programmability and narrow angular selectivity. Here, we report geometrically programmed wrinkle architectonics in a UV-curable chitosan (UVCC)/polydimethylsiloxane (PDMS) bilayer, where lithography-defined, “geometric voids” serve as boundary constraints and stress concentrators to deterministically steer wrinkle trajectories during mechanical deformation. Simple geometric primitives effectively reshape the local stress field through geometrically incompatible confinement, transforming 1D gratings into shape-specific, spatially bifurcated domains characterized by curvilinear orientation and straight-wrinkle fields. We quantify these trajectory fields using the curvature metric and map critical design levers, such as inter-pattern spacing, UVCC thickness, and pattern asymmetry, that modulate curvature magnitude and steering range without altering diffraction periodicity. Circular perimeters sustain extended semi-circular trajectories, whereas polygonal vertices concentrate stress to induce aggressive local curvature. This programmed orientation dramatically amplifies angular dispersion: symmetric circular arrays achieve peak viewing-angle responsivity nearly three times higher than that of unpatterned thin films. A macaw security motif confirms wide-angle azimuthal readability and the formation of device-unique optical fingerprints for anti-counterfeiting. This work transforms a ubiquitous mechanical instability into a geometry-addressable photonic platform for high-level authentication and advanced displays. Hidden optical images that appear only when a transparent film is bent could provide a new generation of anti-counterfeiting technologies. Researchers at UNIST have demonstrated a strategy for programming the microscopic wrinkle patterns responsible for structural color, enabling optical films with enhanced viewing-angle sensitivity and unique optical fingerprints. Led by Professor Taesung Kim of the Department of Mechanical Engineering, the researchers discovered that simple geometric patterns can precisely control how microscopic wrinkles form as the film bends. By reshaping local mechanical stresses, the patterned boundaries guide wrinkle growth into both straight and curved domains while preserving the regular spacing responsible for structural color. This independent control over wrinkle orientation expands the film's optical capabilities. The enhanced wrinkle architecture translated directly into a broader optical response. The engineered films required only seven degrees of viewing-angle change to display the full visible color spectrum, compared with approximately thirty degrees for conventional wrinkle-based films. Distinct color changes also appeared with angular shifts as small as one degree, allowing substantially more optical information to be encoded within the same viewing range. To demonstrate these capabilities, the team fabricated a transparent security film featuring a hidden macaw image that appeared only when the film was bent. Unlike conventional wrinkle-based security films, which are visible only over a narrow range of viewing angles, the new design maintains clear image visibility and vivid structural colors across viewing angles from 0° to 90°. Each film also developed subtle variations in wrinkle branching, creating device-specific optical fingerprints that provide an additional layer of authentication beyond the visible security image. The films also showed excellent mechanical durability, maintaining stable wrinkle patterns and optical performance after 500 repeated bending cycles. “Instead of treating wrinkles as an unavoidable mechanical instability, we showed that they can be programmed through geometric design,” said Professor Kim. “By controlling wrinkle orientation while preserving wrinkle periodicity, we significantly broadened the optical response of wrinkle-based structural color films.” He added, "The approach could be applied to anti-counterfeiting technologies for banknotes, identification cards, luxury goods, and pharmaceutical packaging, while also providing a versatile platform for optical sensors and flexible display technologies." The findings were published in Advanced Functional Materials on June 25, 2026. Dr. Kaliannan Thiyagarajan and Sungjoon Ji from UNIST served as co-first authors of the study. The research was supported by the National Research Foundation of Korea (NRF) and UNIST. Journal Reference Kaliannan Thiyagarajan, Sungjoon Ji, Jiseok Han, et al ., “Geometrically Programmed Wrinkle Architectonics for High Angle-Dependent Structural Coloration,” Adv. Funct. Mater. , (2026).

2026년 07월 14일 화요일

New Wearable Patch Combines Multimodal Sensing with On-Chip AI

Abstract A proposed chest patch device and its interface IC are configured to allow detection of multi-domain signals in the form of optical, electrical, acoustic, and chemical signals at a single body spot. The IC integrates on-chip classification capabilities for cardiovascular diseases (CVDs) based on photo-plethysmogram (PPG) and electrocardiogram (ECG), along with hazardous gas analysis. For low-power multimodal sensing, an ECG R-peak_triggered PPG window (RPT-PW) is proposed as an inter-sensor scheme to reduce activity on the PPG channel, which is the dominant energy consumer. Additionally, the PPG and ECG readout channels incorporate analog peak detection to enable normalized on-chip extraction of pulse arrival time (PAT) and RR interval (RRI). For minimal computation and communication power, a multi-domain convolutional neural network (MD-CNN) processes both analog and digital inputs, and a reconfigurable analog ternary/binarized neural network (a-TNN/BNN) provides additional computation capability for multi-domain applications. The IC was fabricated in an 180-nm BCD process and integrated into a chest patch device prototype with an in-house adhesive meta patch. The RPT-PW demonstrated adaptive operation with an effective LED duty cycle (EDC) as low as 0.02%. The on-chip computation achieved average sensitivity and specificity of 90.87%/95.38% for three-label hypertension, 92.80%/96.36% for three-label arrhythmia, and 92.46%/97.50% for four-label gas mixture classification. Continuous wearable monitoring often requires transmitting large volumes of sensor data to external devices for analysis, increasing power consumption and limiting operating time. A joint research team, led by Professor Jae Joon Kim of Electrical Engineering and Professor Hoon Eui Jeong of Mechanical Engineering, has developed a wearable chest patch that analyzes physiological and environmental signals directly on the device, reducing both communication and sensing power while enabling continuous real-time monitoring. The wearable integrates optical, electrical, acoustic, and chemical sensing into a single device capable of simultaneously monitoring cardiovascular signals and hazardous gases. Rather than transmitting raw sensor data, it processes the information using an on-chip AI processor and sends only the analysis results via Bluetooth. This reduces communication overhead while allowing multiple users to be monitored remotely. To extend battery life, the researchers introduced an adaptive sensing scheme that selectively activates the optical sensor—the most power-intensive component of the device. By synchronizing optical measurements with ECG signals, the system reduced power consumption in the optical sensing channel by approximately 83%, enabling longer operation on a single charge. The device achieved more than 90% sensitivity and specificity in classifying hypertension and cardiac arrhythmias. It also classified hazardous gas mixtures with 92.46% accuracy, demonstrating its ability to monitor both physiological conditions and environmental hazards using a single wearable platform. The chest patch incorporates a microstructured adhesive interface that conforms securely to rough skin while allowing clean removal without leaving residue, making it better suited for long-term wear. “This work brings multimodal sensing and AI inference together on a single wearable platform,” said Professor Kim. “By processing information directly on the device, the system reduces communication demands while enabling continuous monitoring of both health conditions and environmental hazards.” The study has been participated by Sanghyeon Cho and Hyunjoong Kim as co-first authors. Their findings will appear in the July 2026 issue of the IEEE Journal of Solid-State Circuits (JSSC) , one of the leading journals in integrated circuit design. The technology has been licensed to Anvix Lab, a startup co-founded by the research team, which is pursuing commercialization of next-generation bioelectronic patch platform based on on-chip AI. The research was supported by the Ministry of Trade, Industry and Energy (MOTIE), the Ministry of Education (ME), and the Ministry of Science and ICT (MSIT). Journal Reference Sanghyeon Cho, Hyunjoong Kim, Dong Kwan Kang, et. al., “An Energy-Efficient Chest Patch Interface With Inter-Sensor PPG Windowing and Multi-Domain On-Chip Analog Computing,” JSSC , (2026).

2026년 06월 15일 월요일

UNIST Racing Team Takes First Place at International Autonomous Driving Competition

A UNIST autonomous racing team has won first place at RoboRacer, an international autonomous driving competition, held as part of the 2026 IEEE International Conference on Robotics and Automation (ICRA), one of the world's leading conferences in robotics and automation. Team UNICORN Racing , led by Professor Cheolhyeon Kwon of the Department of Mechanical Engineering, claimed the championship title at the competition, which took place in Vienna, Austria, from June 1 to 5, 2026. The team finished ahead of 37 university teams from around the world, demonstrating the strength of its autonomous racing systems under demanding real-world conditions. RoboRacer, formerly known as F1TENTH, challenges teams to develop perception, planning, and control systems for autonomous one-tenth-scale race cars operating under high-speed racing conditions. This year's course introduced significant new challenges. Unlike a conventional flat track, the racing environment included ramps, bridges, and elevation changes that caused vehicles to lift off, land, and experience sudden changes in posture. Success depends not only on speed, but also on the vehicle's ability to perceive its surroundings, accurately estimate its position, and maintain stable control under rapidly changing conditions. Team UNICORN Racing distinguished itself with a technical approach not used by any other team in the competition that was a three-dimensional (3D) LiDAR system. While most teams relied on conventional two-dimensional LiDAR sensors, the UNIST team used 3D LiDAR to capture richer spatial information about the racing environment. The approach addresses a broader challenge in autonomous systems research. In complex three-dimensional environments, changes in terrain and vehicle motion can disrupt perception and localization, making reliable navigation difficult. Processing large volumes of three-dimensional sensor data in real time also requires substantial computing power, particularly on small autonomous platforms with limited onboard resources. To overcome these challenges, the team developed an integrated system that combines 3D perception, localization, path planning, and vehicle control within a compact computational framework. The system maintained accurate localization and stable control even on sections with sharp elevation changes and landing impacts, enabling consistent performance throughout the race and helping secure the overall victory. “This achievement is especially meaningful because it builds on last year's second-place finish and demonstrates the continued progress of our autonomous driving research,” said Professor Kwon. "Our goal is to advance technologies that enable fast and reliable autonomous driving in complex three-dimensional environments." RoboRacer began at the University of Pennsylvania in 2016 and has since grown into a global research and education platform involving more than 100 universities worldwide. By combining real-world racing conditions with autonomous systems research, the competition provides a demanding testbed for technologies that may inform the future of intelligent mobility.

2026년 06월 10일 수요일

UNIST–Fraunhofer Composite Materials Research Partnership Renewed for Third Five-Year Term

The Fraunhofer Innovation Platform for Composites Research@UNIST (FIP-Composites@UNIST) has secured a third five-year term from Germany's Fraunhofer-Gesellschaft following a successful review by Fraunhofer headquarters. The renewal recognizes the center's contributions to composite materials research, industry engagement, and international cooperation, and will support expanded research and training activities over the next five years. Established in 2016 through a partnership between UNIST and the Fraunhofer Institute for Chemical Technology (Fraunhofer ICT), FIP-Composites@UNIST conducts research in lightweight structures, advanced manufacturing, composite recycling, structural health monitoring, and AI-enabled predictive maintenance. Professor Young-Bin Park from the Department of Mechanical Engineering at UNIST serves as director of the center. During its first two phases (2016–2025), FIP-Composites@UNIST established dedicated research facilities and advanced manufacturing and testing capabilities while supporting international research projects, researcher exchanges, and technology development for the Korean industry. Through its partnership with Fraunhofer ICT, the center has built an international research network in composite materials and helped connect Korean companies with advanced manufacturing expertise from Germany. These achievements contributed to the center's successful evaluation by Fraunhofer headquarters and the approval of a third five-year term. Led by Professor Young-Bin Park of the Department of Mechanical Engineering, the center will continue advancing research in composite materials and manufacturing technologies while strengthening industry partnerships and expanding training opportunities for students and researchers. The renewal coincided with the recent FIP in Korea Networking program, which brought together representatives from Fraunhofer headquarters, German research institutes, and Korea-based Fraunhofer Innovation Platforms. UNIST hosted the program's second day, where participants discussed ways to expand cooperation across Fraunhofer's Korean network. “UNIST has demonstrated strong achievements in international research cooperation,” said Frauke Mickler, Program Manager for International Research Collaboration at Fraunhofer-Gesellschaft. “We hope FIP-Composites@UNIST will continue to serve as a leading model for Fraunhofer's international partnerships worldwide.” UNIST President Chong Rae Park welcomed the renewal and reaffirmed the university's commitment to the partnership. "This renewal reflects the strength of the partnership that UNIST and Fraunhofer have built together,” said President Park. “We look forward to expanding joint research and creating new opportunities for researchers, students, and industry partners in the years ahead.

2026년 05월 12일 화요일

Ultrafast 3D Microfabrication Technology Produces Complex Structures in Just 60 Seconds

Abstract Despite the advantages of additive manufacturing (AM) in creating customized 3D shapes, conventional layer-by-layer approaches are limited by low production rates, restricting their broader applications. Volumetric additive manufacturing (VAM) has emerged as a promising technique, enabling the simultaneous photopolymerization of entire volumes, which significantly reduces fabrication time. However, current computed axial lithography requires manual operations per print cycle, such as loading resin into a vial, physically placing and aligning the vial (with or without an index-matching medium), and removing the printed object, limiting continuous, high-throughput production of multiple parts. Here, we propose a dispensing volumetric additive manufacturing (DVAM) method that prints and dispenses each part within a droplet in less than a minute. The printing process occurs within a single droplet dispensed from a glass pipette, enabling simultaneous printed object removal and resin replenishment in a second. Light pattern distortion caused by the absence of the index-matching fluid was corrected through real-time droplet profile estimation and inverse ray-tracing within the optical system. We demonstrate rapid serial VAM of 10 different objects within 10 min. This approach establishes a practical pathway toward scalable, high-throughput volumetric manufacturing, enabling rapid production of complex 3D structures without the operational bottlenecks of conventional VAM workflows. A research team affiliated with UNIST has achieved a major breakthrough in 3D microfabrication, enabling the rapid production of intricate 3D structures within a single minute. The new technology promises to significantly accelerate manufacturing processes in fields, ranging from biomedical devices to microelectronics. Led by Professor Im Doo Jung from the Department of Mechanical Engineering at UNIST, the research team developed a cutting-edge dispensing volumetric additive manufacturing (DVAM) system.The new approach moves beyond traditional layer-by-layer methods, allowing for the swift, continuous fabrication of diverse three-dimensional shapes within a matter of minutes by employing a volumetric printing process. DVAM utilizes a fine resin droplet dispensed from a glass pipette as the entire build volume. Light is projected onto the droplet to cure the desired shape in real-time. Once a structure is solidified, compressed air expels the finished object, and a new droplet is immediately dispensed for the next cycle. This seamless process enables high-speed, continuous production without the need for post-processing or layer-by-layer assembly. Unlike conventional volumetric printing methods—such as Computed Axial Lithography (CAL)—which require filling a resin container, matching refractive indices with immersion fluids, and manually removing finished parts, the UNIST team’s approach eliminates these constraints. By performing printing within a single resin droplet that acts as the entire volume, they achieve rapid, uninterrupted manufacturing. One of the main challenges was optical distortion caused by the droplet’s curved surface. To overcome this, the researchers integrated artificial intelligence with inverse ray-tracing optical calculations. A deep learning-based AI accurately detects the droplet’s profile in real-time, enabling the system to mathematically correct for distortions and project precise light patterns for uniform curing. This advanced system successfully produced intricate structures, such as the Eiffel Tower and The Thinker, within just 10 minutes—demonstrating the ability to fabricate ten different objects with an average time of around 60 seconds each. Hongryung Jeon, the first author of the study, explained, “Unlike traditional 3D printing that builds objects layer by layer, our method cures the entire volume at once and continuously dispenses resin without additional post-processing, increasing speed by over 100 times. This opens up exciting possibilities for large-scale, rapid production of micro-scale components.” Professor Jung, the corresponding author, emphasized, “Speed has long been a limiting factor in 3D printing, especially for customized manufacturing. By moving away from conventional photopolymerization and employing artificial intelligence to compensate for optical distortions, we have significantly advanced the potential of ultrafast volumetric fabrication. Now, complex shapes can be produced in seconds—eliminating long wait times.” The findings of this research have been published online in Advanced Functional Materials (Impact Factor: 19.0, top 5% in JCR) on March 21, 2026. The project was supported by the National Research Foundation of Korea (NRF), the Ministry of Science and ICT (MSIT), the Institute for Information & communications Technology Planning & Evaluation (IITP), and the Ministry of Trade, Industry & Energy (MOTIE). Journal Reference Hongryung Jeon, Yunsoo Lee, Seobin Park, et al., "Dispensing Volumetric Additive Manufacturing," Adv. Funct. Mater., (2026).

2026년 04월 24일 금요일

UNIST to Establish KRW 38.2 Billion AI Ship Technology Testing and Validation Center

UNIST has secured a government-funded project to establish a testing and validation center for AI-based ship equipment and advanced components, marking a major step toward advancing Korea’s shipbuilding industry. The project, supported by the Ministry of Trade, Industry and Energy (MOTIE), was selected under a 2026 government initiative to strengthen infrastructure in the shipbuilding and offshore sector. With a total budget of KRW 38.2 billion, it will support the development and validation of AI-driven autonomous ship systems and advanced composite materials in response to tightening environmental regulations led by the International Maritime Organization (IMO), as well as evolving industry demands. Scheduled for completion by 2030, the center will be located in the Ulsan Mipo National Industrial Complex. UNIST will oversee its construction and operation in collaboration with the Ulsan Metropolitan Government, with participation from Ulsan Technopark, the Korea Testing & Research Institute (KTR), and the Korea Research Institute of Ships and Ocean Engineering (KRISO). Designed as an end-to-end validation platform, the facility will support the full development cycle—from design and prototyping to performance testing and certification readiness. In the advanced components domain, it will enable the validation of fiber-reinforced plastic (FRP)-based ship structures, exterior materials, and propulsion components under real-world operating conditions, including mechanical stress, corrosion, and cryogenic environments. The center will also incorporate AI- and digital twin-based manufacturing systems to enhance process optimization, quality control, and productivity. For AI-based ship systems, the center will establish a hybrid validation environment combining simulation and field testing, supporting the evaluation of autonomous navigation algorithms, sensor modules, and control systems in terms of safety, reliability, and repeatability, and accelerating commercialization. The center is expected to play a key role in supporting small and mid-sized shipbuilding suppliers by improving access to testing infrastructure and validation data, thereby strengthening industrial competitiveness and facilitating entry into global markets. Professor Young-Bin Park of the Department of Mechanical Engineering at UNIST, who leads the project, said, “This initiative will help bridge the gap between technology development and commercialization, supporting the advancement of environmentally sustainable and intelligent shipbuilding."

2026년 04월 23일 목요일

UNIST Unveils Smart Contact Lens with Meniscus Pixel Printing for Vision-Based Robotic Control

Abstract Contact lenses are emerging as strong candidates for next-generation extended reality (XR) interfaces due to their lightweight and ergonomic form factor. However, integrating photodetector arrays onto the limited area of a lens remains challenging with conventional micropatterning approaches, which rely on masks, multistep processes, and specialized equipment that inherently limit throughput and scalability. To address these constraints, we introduce a Meniscus Pixel Printing (MPP) strategy that enables rapid, mask-free patterning of MAPbI3 perovskite photodetectors without costly or complex fabrication tools. MPP uses a self-confined meniscus at a pipette tip to deterministically transfer perovskite ink, enabling 200 µm pixels to be printed within 1 s per pixel. In addition to planar substrates, MPP demonstrates stable pixel patterning on curved surfaces, highlighting its geometric adaptability and process versatility. Using this approach, we fabricate a 10 × 10 perovskite photodetector array and demonstrate stable photoresponse, retaining 92% of its initial performance after two months of storage. To overcome limited pixel density, a deep-learning-based super-resolution (SR) model reconstructs 10 × 10 inputs into 80 × 80 optical information with 97.2% accuracy and 0.03 s latency. Additionally, an AI-based eye-tracking system recognizes nine eye gestures with 99.3% accuracy, enabling smooth hands-free robotic arm control. A research team, led by Professor Im Doo Jung from the Department of Mechanical Engineering at UNIST, has developed a groundbreaking smart contact lens that enables users to control robots through eye movements. This innovative device combines embedded optical sensors with AI-based signal processing, offering a lightweight, intuitive human-machine interface with vast potential across industries. The lens incorporates a 10×10 array of light sensors capable of detecting subtle changes in light distribution caused by eye movements, including gaze direction and blinks. These signals are transmitted to control external robotic systems, as demonstrated with a robotic arm. Notably, the team employed a novel Meniscus Pixel Printing (MPP) technique to directly print sensors onto the curved lens surface without masks or complex fabrication steps, ensuring high precision and customizability. In addition to robotic control, the system demonstrates vision sensing capabilities by reconstructing optical information. To address the limited signal resolution inherent to micro-scale devices, the researchers applied deep-learning-based super-resolution algorithms, reconstructing high-fidelity signals equivalent to an 80x80 sensor array in just 0.03 seconds. This enables real-time, accurate control based solely on eye movements, achieving recognition accuracies of up to 99.3% under experimental conditions. This technology marks a significant advancement in ultra-compact human-machine interfaces, enabling precise, hands-free control of electronic devices. Potential applications include remote robotic operation, medical assistive devices, exploration in hazardous environments, defense systems, and smart mobility. Published in the March 2026 issue of Advanced Functional Materials (Impact Factor: 19.0, JCR Top 5%)—a top-tier journal in materials science—the research was selected as the Front Cover of the latest issue. The study received support from the National Research Foundation of Korea (NRF), the Ministry of Science and ICT (MSIT), the Institute of Information & Communications Technology Planning & Evaluation (IITP), and the Ministry of Trade, Industry, and Energy (MOTIE). Journal Reference Byung-Hoon Gong, Dohyean Kim, Jiyun Jeong, et al ., “Meniscus Pixel Printing for Contact-Lens Vision Sensing and Robotic Control,” Adv. Funct. Mater. , (2026).

2026년 04월 13일 월요일

UNIST Launches Major AI Initiative to Transform Shipbuilding Industry

UNIST has launched a major interdisciplinary research initiative to accelerate AI-driven transformation (AX) across the shipbuilding sector. Selected under the Ministry of Science and ICT’s national initiative for large-scale industrial AI, the project is supported by a total budget of KRW 40.3 billion (approximately USD 30 million). It aims to develop domain-specific AI foundation models trained on real-world shipyard data and deploy them in operational environments. The initiative brings together faculty from the Graduate School of Artificial Intelligence (AIGS), along with multiple engineering and business disciplines, forming a collaborative structure that spans the full lifecycle of industrial AI—from data generation and model training to system deployment and real-world validation. At the core of the project is the development of a multimodal, domain-specific foundation model for shipbuilding. This effort is led by Professor Sung Youb Kim from the Graduate School of Semiconductor Materials and Devices Engineering, with participation from Professors Seung-Hoon Na, Youngsoo Jang, Seungryul Baek, and Taehwan Kim from AIGS, along with Professor Hayoung Chung from the Department of Mechanical Engineering. The team will develop a model capable of integrating diverse data generated across shipyard operations, serving as a central engine for a wide range of downstream applications. Complementing this effort, a parallel workstream led by Professors Sung Whan Yoon, Jae-Young Sim, and Yeon-Chang Lee from AIGS, along with Professor Saerom Park (Department of Industrial Engineering) and Professor Seongil Wi (Department of Computer Science and Engineering) will establish a scalable industrial platforms for optimized for heterogeneous datasets. In parallel, Professors Seungjoon Yang, Gi-soo Kim, and Yeon-Chang Lee from AIGS will lead research on synthetic data generation, addressing limitations in real-world data and improving model robustness across diverse scenarios. Application-focused research will be led by Professors Youngdae Kim, Sungil Kim, Hyungho Na, Dong Young Lim, and Chiehyeon Lim from the Department of Industrial Engineering, along with Professor Junghoon Kim (Department of Computer Science and Engineering), Professor Jinyoung Choi (AIGS), and Professor Junhyoung Ha (Department of Mechanical Engineering). Their work will focus on manufacturing optimization, production scheduling, predictive maintenance, and the intelligent operation of collaborative robotic systems. The development of lightweight, on-device AI technologies will be led by Professors Taesik Gong (Department of Computer Science and Engineering), Jongeun Lee (Department of Electrical Engineering), and Ranggi Hwang (Department of Computer Science and Engineering). Meanwhile, Professors Byeong Ki Seo (School of Business Administration) and Namhun Kim (Department of Mechanical Engineering) will oversee field validation and service deployment to ensure practical application and scalability. To ensure real-world impact, the initiative will be carried out in close collaboration with industry partners, including HD Hyundai Heavy Industries, HD Korea Shipbuilding & Offshore Engineering, and CrowdWorks. By leveraging operational data from active shipyards, the consortium will validate and refine technologies through on-site implementation. Professor Seung-Hoon Na, who coordinates the overall project, stated, “This initiative goes beyond developing AI models—it establishes a foundation for industrial AX. By integrating AI into production planning, process optimization, and quality control, we expect to drive meaningful innovation across the shipbuilding industry.” The UNIST Office of Research Affairs supported the initiative through its Pre-Award pilot program, providing strategic and administrative assistance in proposal development, budgeting, and coordination. The university plans to further advance its research management framework to support large-scale, high-impact initiatives.

2026년 03월 31일 화요일

UNIST-Nurtured Startup RecensMedical Debuts on KOSDAQ!

A startup incubated at UNIST has successfully listed on the KOSDAQ, marking a significant milestone in the university's efforts to translate research into commercially viable technologies and foster globally competitive ventures. RecensMedical Inc., a faculty-founded startup established in 2016, began trading on the KOSDAQ on March 31, 2026. The company is based on proprietary research developed in the laboratory of Professor Gun-Ho Kim of the Department of Mechanical Engineering at UNIST. The company specializes in precision cooling-based medical devices for dermatology, ophthalmology, and veterinary applications. Its core technology enables real-time monitoring of tissue temperature and precise control of cooling intensity and duration, offering an alternative to conventional chemical anesthesia by reducing patient discomfort and improving procedural efficiency. "RecensMedical exemplifies how UNIST's foundational research can be transformed into impactful, market-ready solutions," said Professor Gun-Ho Kim, CEO of RecensMedical Inc. "We will continue to accelerate global expansion by securing regulatory approvals and strengthening international distribution channels." The company's public listing underscores the effectiveness of UNIST's R&D Scale-Up Strategy, which integrates the full innovation pipeline—from fundamental research and intellectual property development to startup incubation, manufacturing, and global market entry. UNIST has systematically supported technology commercialization by building strong patent portfolios from early-stage research, providing on-campus incubation infrastructure, and offering programs for prototyping, investment attraction, and overseas expansion. RecensMedical Inc. benefited from this ecosystem throughout its growth. In particular, UNIST’s GMP-certified manufacturing facilities played a critical role in securing regulatory approval from the Ministry of Food and Drug Safety (MFDS), enabling both product validation and commercialization. The company has also achieved steady growth through continued institutional and external support. It was selected as the first TIPS (Tech Incubator Program for Startup) in Ulsan in 2017 and later secured investment from a fund managed by UNIST Holdings in 2022. Additional support was provided through the Ulsan-Ulju INNOTOWN, where UNIST serves as a core technology institution. The initiative offers access to advanced research infrastructure, demonstration facilities, and R&BD funding, while facilitating connections to private investment and global mentoring networks. President Chong Rae Park stated, "UNIST's R&D Scale-Up Strategy emphasizes commercialization from the earliest stages of research. RecensMedical's growth demonstrates how integrated support—from technology validation to global collaboration—can translate research into real industrial impact. We will continue to strengthen an innovation ecosystem that connects research, industry, and global markets." RecensMedical Inc. has developed a portfolio of precision cooling devices designed to reduce pain and shorten treatment time across multiple clinical fields. Its flagship products include TargetCool for dermatology, OcuCool for ophthalmology, and VetEase for veterinary care. Following its KOSDAQ debut, the company is positioned to accelerate global commercialization and further establish itself as a leader in precision cooling medical technology.