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

SungJin's Board to Board Connector(SJB) is a 2mm pitch D-SUB connector. The SJB has been developed for the aerospace, avionics, instrumentation & control, defence and transportation markets, with applications that include flight controls, radars, missiles, electro-optical sighting systems, satellites, mobile radios, ower supplies and engine controls. It is also used in a growing number of medical, and telecom applications. SungJin's Board to Board Connector(SJB) are designed to meet or exceed the relevant electrical and environmental performances described in MIL-DTL-55302F standards.

A wire harness in military or aerospace applications is an assembly of wires, cables, connectors, and protective components designed to organize and route electrical and signal connections efficiently

Ultra-High Density: With a pin spacing of 0.025 inches, it achieves maximum miniaturization and lightweight design. Ultimate Reliability: Designed to ensure perfect signal integrity even in harsh environments. Durability: Boasts outstanding environmental resistance that meets military and aerospace standards.

This fully self-contained pressure test and calibration system combines pressure generation, signal measurement, and loop power to deliver all the convenience of the Druck DPI 610/615, while being half the size, twice as accurate and easy to use. The DPI 611 is the fourth generation of the DPI 600 family, first introduced in 1984. The DPI 600 family revolutionized testing and calibration by providing all the tools for pressure generation and signal measurement in a stand-alone portable package. The DPI 600 soon became the flagship equipment of the industry and today it is simply known as "Druck". Built on a technical legacy and more than 30 years of experience in pressure measurement and calibration, the DPI 611 offers all the convenience and reliability of a true "Druck", while delivering double the performance of a product half its size.

ZYX SPACE is an intelligent design platform that integrates artificial intelligence into the architectural design process. It is designed to minimize repetitive tasks through automation and empower designers to focus on creativity and decision-making. With its advanced AI engine trained on over 80,000 architectural drawings, ZYX SPACE can automatically recognize 2D floor plans, identify key elements such as walls, doors, and windows, and generate accurate 3D models in real time. Designers can experience seamless 2D-to-3D transformation without the need for manual modeling. ZYX SPACE also offers powerful daylight analysis features, simulating natural sunlight movement to visualize indoor lighting conditions and energy efficiency through heatmaps. This enables informed design decisions that consider both aesthetics and sustainability. One of the platform’s standout capabilities is converting image-based drawings into CAD-compatible vector files. ZYX SPACE not only extracts geometric data but also recognizes numerical information embedded in scanned or photographed blueprints, making legacy data usable in modern design workflows. Through auto-learning and labeling features, the platform continuously improves its recognition accuracy by learning from accumulated project data. It automatically classifies architectural components and spatial configurations, boosting productivity and reducing errors. ZYX SPACE also supports AI-driven interior rendering. It analyzes room layouts to suggest optimal furniture placements and delivers real-time visualizations based on different interior concepts, helping designers and clients envision the final space with clarity. When combined with ZYXCAD, ZYX SPACE provides a highly integrated design environment—enabling end-to-end automation from drafting to visualization. This powerful synergy allows architects, engineers, and designers to accelerate their workflows, improve precision, and unlock new levels of creativity.

In collaboration with Hanwha Systems, we developed and optimized a Transformer-based AI model for ship and aircraft detection on embedded platforms. Targeting NVIDIA Jetson Orin AGX, the project utilized the MSAR (640×640) dataset along with large-scale training databases built from public sources. The RT-DETR model, currently under review, was optimized through hardware-friendly design, Int8 quantization, and specialized inference acceleration. As a result, the solution achieved up to 5x faster inference speed while maintaining accuracy, reaching 20% of NVIDIA TensorRT performance. This demonstrates the ability to deliver reliable, real-time detection capabilities in resource-constrained embedded environments, enabling next-generation defense and surveillance applications.