An AI‐based Decision Support System for Naval Ship Design

Chou, Yu-Chao; Benjamin, Colin

doi:10.1111/j.1559-3584.1992.tb02235.x

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…ț HOSDES for preliminary design (Koops, 1985) ț DESIGNER for preliminary numerical design (MacCallum & Duffy, 1986) ț Preliminary design using object-oriented knowledge representations (Akagi & Fujita, 1990) ț Hydrodynamic design (Priha, 1989) ț A decision support system to provide decision support at the early stage of naval ship design (Chou & Benjamin, 1992) Since 1994, papers on ship-design expert systems have covered all design phases, and the number of articles has increased.…”

Section: Related Work On Ship-design Expert Systemsmentioning

confidence: 99%

Overview of ship‐design expert systems

Park

Storch

2002

Expert Systems

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Expert systems constitute a branch of artificial intelligence, and their unique characteristics enable computer systems to perform at the level of a human expert by the use of algorithms that can capture domain‐specific knowledge. Through an extensive survey of research about ship‐design expert systems, we conclude that although development and implementation of expert systems for ship design offer substantial barriers, the shipbuilding industry needs them nonetheless. We examine the major reasons that use of expert systems in this industry has lagged behind. These include the problems of integration with existing computer‐aided design systems, problems of knowledge representation and sizable development costs. We discuss in detail some key factors that can lead to the solution of these problems.

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Section: Related Work On Ship-design Expert Systemsmentioning

confidence: 99%

Overview of ship‐design expert systems

Park

Storch

2002

Expert Systems

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“…The application research for the design of a system configuration is relatively active compared to other fields. A systematic procedure in the form of an interactive design decision support system (DDS) incorporating artificial intelligence (AI) and other modeling tools, such as a spreadsheet and a database management system, to provide effective decision support at an early stage of design has been developed (Chou and Benjamin 1992). This system contains ship design heuristics and related models that can aid a ship designer in rapidly evaluating design modifications.…”

Section: Applications Of Knowledge-based Systems In Military Vesselsmentioning

confidence: 99%

Decision‐Aid System for Survivability of Damaged Conventional Military Submarines

Lee¹,

Lee

Lee³

2002

Naval Engineers Journal

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Even small leakage in submarines can lead to serious consecutive damage, since they operate under high water pressure. Such leakage including damage to piping and the hull eventually result in human casualties and loss of expensive equipment as well as the loss of combat capabilities. In such cases, a decision‐making system is necessary to respond immediately to the damage in order to maintain the safety or the survival of the submarine. So it is necessary to have an automatic system that can generate responses and give advice to the operator on how to make decisions to maintain the survivability of the damaged vessel. In this paper, a knowledge‐based decision support system for submarine safety is developed. The domain knowledge is acquired from submarine design documents, design expertise, and interviews with operators. The knowledge consists of responses regarding damage of the pressure hull and piping systems. Expert elements are deduced to obtain the decision from the knowledge base, and the system makes recommendations on how to cope with damage to the hull and piping and whether the submarine should stay in the sea or to surface. It is demonstrated that the developed system is well simulated to the real situation through sample applications.

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“…Naval ship design is a complex and monumental task. Chou et al [10] used AI to design modular naval ships, and the results were accurate, efficient, and interaction-friendly. Advances in computer vision may replace navigators.…”

Section: Introductionmentioning

confidence: 99%

Ship Network Traffic Engineering Based on Reinforcement Learning

Yang,

Liu,

Wang

et al. 2024

Electronics

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This research addresses multiple challenges faced by ship networks, including limited bandwidth, unstable network connections, high latency, and command priority. To solve these problems, we used reinforcement learning-based methods to simulate traffic engineering in ship networks. We focused on three aspects—traffic balance, instruction priority, and complex network structure—to evaluate reinforcement learning performance in these scenarios. Performance: We developed a reinforcement learning framework for ship network traffic engineering that treats the routing policy as the state and the network state as the environment. The agent generates routing changes and uses actions to optimize traffic services. The experimental results show that reinforcement learning optimizes network traffic balance, reasonably arranges instruction priorities, and copes with complex network structures, greatly improving the network’s quality of service (QoS). Through an in-depth analysis of the experimental data, we noticed that network consumption was reduced by 9.1% under reinforcement learning. Reinforcement learning effectively implemented priority routing of high-priority instructions while reducing the occupancy rate of the edge with the highest occupancy rate in the network by 18.53%.

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An AI‐based Decision Support System for Naval Ship Design

Cited by 9 publications

References 9 publications

Overview of ship‐design expert systems

Overview of ship‐design expert systems

Decision‐Aid System for Survivability of Damaged Conventional Military Submarines

Ship Network Traffic Engineering Based on Reinforcement Learning

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