260915 KOMSA AI어선안전설계데이터플랫폼 2화 진수식 송출용

Safety Philosophy: Move Beyond Experience-Only Design

• The video argues that safety for small fishing vessels should not rely solely on experience or tradition.
• Instead, it should be based on systematized, data-driven design.
• Ship safety depends on many interacting factors—such as:
  • Hull shape
  • Propeller size
  • Cabin placement
  • Location of lifting equipment
• These elements must be modeled and integrated as data, not treated as isolated choices.

KOMSA AI Fishing Vessel Safety Design Data Platform (Episode 2): Main Technological Concepts

• The platform introduced 156 engineered “design modules” to digitize vessel components and make design outcomes safer and more repeatable.
• It is framed as an intelligent, modular “library”:
  • Vessel equipment (e.g., hull, engine, cabin, ejection/lifting gear) is split into verified independent pieces.
  • Users can effectively “assemble” a vessel by selecting components like Lego blocks.
  • The system performs real-time safety calculations and checks rollover risk in advance, regardless of component combinations.

Problem the Research Addresses

Even though South Korea is a leading shipbuilding nation, the small fishing boat design sector faces challenges such as:

• Small-scale industry structure
• Labor shortages
• Outdated equipment
• A continuing issue where unreasonable demands from shipowners get reflected in design drawings in ways that can increase danger

Hull Module Development (Most Complex / Foundational)

• The hull is described as the design “root,” strongly influencing both:
  • Safety
  • Performance
• The team aimed to build an optimized hull module by calculating environmental variables for Korea’s seas, including:
  • Open sea waves
  • Shallow waters in the West Sea
  • How different sea conditions combine
• A major method used:
  • Tank experiments to reproduce actual sea conditions
  • Verification that simulation results match engineering standards
• Data reliability steps included:
  • Validating the physical meaning and overall trends of simulations
  • Cross-checking simulations against model test results
  • Mention of 108 verified linear visions and sustained work effort (e.g., “2 days of tenacity”)

Workspace / Fishing Systems Modules (Integration Difficulty)

• Beyond the hull, a major challenge was standardizing systems that function as workspaces for fishermen.
• The video emphasizes that not only laboratory/engineering data were used, but also field realities, quantified through:
  • Physical stress during operations, converted into data/statistics
• It mentions 12 types of fishing system modules, designed to:
  • Increase work efficiency
  • Reduce risk of accidents

Overall Goal

• The project aims for an AI-based “optimal design” approach, including stability (not just performance).
• The intended outcome is a safer ocean with fewer maritime accidents.
• The modules are positioned as a foundation for protecting fishermen in real operating environments.

Main Speakers / Sources (Implied)

• Narrator / research team presenter (primary voice throughout)
• Ship safety / research institute (the organization driving the platform and ecosystem creation)
• Fishing vessel fishermen (their experience and operational stress data were incorporated, though not presented as named speakers)