Original compact design and long-term production know-how. System reliability and overall competitiveness.

1. Integrated Production System Equipped with in-house CF-SMC compression molding equipment, we provide a complete one-stop solution—from sheet production to final composite molding—tailored to customer requirements. This ensures both exceptional quality and production efficiency. In line with ESG management, we also utilize our proprietary recycled carbon fiber technology to recycle CFRP waste generated during CF-SMC production. 2. Moldability & Productivity ILSUNG’s carbon fiber SMC technology offers superior moldability compared to conventional continuous CFRP and aluminum. With a rapid curing speed of about 1 minute per 1 mm thickness, it enables high-speed, mass production. Complex and precise geometries can be molded reliably, delivering strong competitiveness for both simple and highly engineered components. 3. Exceptional Lightweight Performance CF-SMC has a lower density than aluminum castings while maintaining high mechanical strength, delivering the optimal lightweight solution for industries where weight reduction is critical. 4. Balanced Performance This technology not only achieves lightweight properties but also ensures a well-balanced combination of mechanical strength, corrosion resistance, dimensional stability, and durability. It maintains stable performance under extreme environments and repeated loads, making it highly suitable for automotive, aerospace, and defense applications where reliability is critical. 5. Cost Competitiveness Compared to CFRP, it provides a lower-cost structure and high-speed molding capability, ensuring excellent price competitiveness. This significantly improves cost efficiency in industries that demand large-scale production—such as automotive, aerospace, and defense—and strengthens its growth potential within the composites market.

IP66-rated waterproof and dustproof design Intel® Alder Lake Core™ i3-N305 with 8 E-cores -40°C to 70°C temperature operation 2x 2.5GbE via M12 X-coded connectors 1x isolated RS-232, 1x isolated RS-422/485 via M12 A-coded connectors 3x isolated DIO and 1x CAN2.0B via M12 A-coded connectors Low power and high endurance FRAM for data logger Class 1, Division 2 and ATEX certification in hazardous area applications 8V to 35V DC input with ignition power control input via M12 A-coded connector

암페놀-대신전자정밀(주)는 Amphenol Corporation의 한국 법인으로, 산업용, 군수용, 이동 통신 및 철도 차량용 커넥터를 주력 제품으로 생산하고 국내 시장 뿐만 아니라 수출 및 해외 암페놀 법인의 제품을 한국 시장에도 공급하는 공식 자회사입니다.

The mighty Hitec SG33BLT Series servo actuators operate on DroneCAN (formerly UAVCAN), CAN 2.0 A/B, RS485, PWM and TTL command protocols, and feature brushless motors, magnetic encoders, and hardened steel gears. They are also environmentally sealed with an IP67 waterproof case rating. The SG33BLT Series was initially developed for the automotive industry requiring minimal support equipment and wiring, saving unnecessary installation complexity, cost and weight. This same functionality is now enjoying widespread adoption of CAN and DroneCAN systems within the UAV environment. With options for both 9~15-volt and 18~32-volt operations, and DB9SUB, Circular, & Gland Cable connectivity, the SG33BLT Actuator Series is sure to have a solution for your needs. #UAV #UAS #UAM #UGV #UUV #USV

· Wide-area situational awareness through omni-directional cameras and EO/IR sensors · Seamless remote control enabled by LEO satellite communication and USV teaming operation · Multi mission capabilities by interchangeable mission modules such as UUV, UAV and compact rocket modules

Ski-Doo·Sea-Doo·Can-Am 등 아이코닉 브랜드를 보유한 글로벌 레저 모빌리티 선도기업으로, 스노모빌·수상 레저·ATV/사이드바이사이드·3륜 모토사이클과 Rotax 엔진까지 폭넓은 제품군을 제공합니다. 방위 산업업체와도 협력을 모색합니다.

1. RCWS - 7.62mm Machine Gun (Compatible of K-16 and M60) - Magazine Capacity : Max. 400 Rounds. 2. EO/IR - Day/Night Range : Detection 4 Km, Recongnition 2 Km - Range Finding Accuracy : less 5m in 4 Km 3. AI Object Recognition - Real-time Object Detection Model - GPU-Based High Speed AI image Processing - Application of Supervised Learning Data