Wheels that can drive the tank

Liquid Measurement Systems (LMS) provides fuel gauging and interface controls for service, maintenance, and flight crew personnel, at the primary refuel point on the aircraft.

External Refuel Preselect Panels can display the quantity of fuel in each tank, calculate the total quantity of fuel onboard, allow service teams to pre-select desired fuel quantity, and display fault codes. LMS’ external refuel preselect panels are compact, lightweight, and rugged systems that can be adapted to a variety of platforms.

It is a product lineup that is proven in global markets and has world-class competitiveness. And it is being exported to more than 20 countries around the world.

This system product is a product that directly supplies and heats lubricant oil for smooth operation of the main engine cylinder’s metallic and mechanical movement. Lubricator, a product that supplies lubrication directly to the main engine cylinder liner, can control injection timing and consumption using an electronic control method that responds to the movement of the main engine piston. The alpha lubricator system is based on an algorithm controlling the cylinder oil dosage proportionally to the sulphur content in the fuel and, fuel consumption on particular load of engine. Minimum cylinder oil dosage is required to provide an adequate oil film and detergency properties. Everllence and WIN GD engines are currently applying the second generation Lubricator type, the Everllence type is Mk-I and the WIN GD type is Mk-ε (Epsilon).

Solution Overview EagleEye is a a synthetic data generation & management platform for AI training that integrates image generation, LLMOps, and MLOps. It automates high‑quality dataset generation, labeling, and model validation—and has been certified “military suitable” and delivered first to the ROK Army AI Center. Main Features Image generation with text/image prompts; object/background customization LLMOps: prompt understanding + image–text labeling workflow MLOps: CV model training/validation, dataset versioning, experiment tracking End‑to‑end pipeline from data preprocessing to model deployment Data augmentation and management for scalable, repeatable training Key Advantages Certified “military suitable”; meets defense operational requirements First AI solution supplied to the ROK Army AI Center, proving reliability One‑stop synthetic data + model ops cuts development/validation time Fully in‑house stack enables rapid updates and mission‑tailored customization 2025: First AI product delivered to the ROK Army AI Center for defense AI projects Used to generate and manage high-quality synthetic imagery for military AI model training and validation

Integrated Procurement Technologies (IPT) is a leading aerospace and military distributor and worldwide supplier of OEM parts and equipment. We specialize in aftermarket and new business solutions for our customers and OEM partners. We are experts in simplifying and expanding the global marketplace.

With an AS9120-certified logistics system and strong semiconductor expertise, we are tailored to support defense and aerospace customers, where small-volume, high-mix requirements, long life cycles, and unpredictable demand are common. Beyond semiconductor components, we also provide electronic instruments and software solutions, serving as a total solution provider.

Supports non-destructive inspection of aircraft composite skin structures and is composed of a piezoelectric layer sensor, data acquisition equipment, and damage analysis software. Utilizes piezoelectric sensors to generate Lamb-wave and acquire signals, collecting data to accurately detect and assess composite damage. Visualizes inspection results on display such as damage location, size and index. - POD Accuracy: 1 inch 90% or more - POD Precision : Position error within 30 mm - 64 channels / 230 path reference detection time within 10 minutes - Sensor : Resonance resistance less than 400kΩ - Sensor : MIL-STD-810G - Sensor : RTCA DO-160 Section 23.0 - DAQ : x128 Piezoelectric sensor interface