3,861 research outputs found

    Accelerating utilization of new materials

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    Rate of introducing new or improved materials in national program

    A geo-database for potentially polluting marine sites and associated risk index

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    The increasing availability of geospatial marine data provides an opportunity for hydrographic offices to contribute to the identification of Potentially Polluting Marine Sites (PPMS). To adequately manage these sites, a PPMS Geospatial Database (GeoDB) application was developed to collect and store relevant information suitable for site inventory and geo-spatial analysis. The benefits of structuring the data to conform to the Universal Hydrographic Data Model (IHO S-100) and to use the Geographic Mark-Up Language (GML) for encoding are presented. A storage solution is proposed using a GML-enabled spatial relational database management system (RDBMS). In addition, an example of a risk index methodology is provided based on the defined data structure. The implementation of this example was performed using scripts containing SQL statements. These procedures were implemented using a cross-platform C++ application based on open-source libraries and called PPMS GeoDB Manager

    ์ „ํˆฌํ•จ์˜ ์šด์˜ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๊ณ ๋ คํ•œ ์Šน์กฐ์› ๊ตฌ์„ฑ ์ตœ์ ํ™”

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์กฐ์„ ํ•ด์–‘๊ณตํ•™๊ณผ, 2023. 2. ๋…ธ๋ช…์ผ.Currently, the military is planning to reduce the number of troops for reasons such as a decrease in the youth population and a shortened service period. However, battleships require more crew than before due to increased size, mounted weapons, and equipment. Therefore, deploying the appropriate number of crew members on the battleships is important. In addition, since battleships must consider various operating situations (combat, maintenance, etc.) and crew members have various specialties, it is essential to optimize the crew's composition to suit the battleships' characteristics. To this end, the Navy relies on experts with relevant know-how and data based on legacy ships. Still, additional optimization is required for reasons such as changes in military policy, enlargement of new battleships, and diversification of weapons. In this paper, given the specifications of the design ship and major mounted equipment, the crew composition is primarily calculated using the data of the militarys legacy ship currently in operation. Since the result was calculated based on the past, the expert system was additionally used to calculate the result reflecting the characteristics of the ship I designed and the current operation of the ship. Afterward, a method of optimizing the composition of the crew was studied using the simulation method. The estimation method based on legacy ship data estimates crew members with various specialties in consideration of ship specifications and loaded weapons and estimates the crew composition suitable for the design ship using regression analysis. The estimation method of an expert system uses rule-based expert systems to re-estimate the crew member composition. The estimation method based on simulation optimizes the composition of the crew by comparing and analyzing mission execution time and efficiency using Discrete Event System specification (DEVS) simulation in consideration of scenarios that mimic the actual operating situation of the ship. Finally, a self-developed program was implemented for verification, and the performance was verified by inputting the specifications of the US Navy ship and the number of crew members into the program.ํ˜„์žฌ ๊ตฐ์€ ์ฒญ๋…„ ์ธ๊ตฌ ๊ฐ์†Œ, ๋ณต๋ฌด๊ธฐ๊ฐ„ ๋‹จ์ถ• ๋“ฑ์„ ์ด์œ ๋กœ ๋ณ‘๋ ฅ ๊ฐ์ถ•์˜ ๊ณ„ํš์˜ ์„ธ์šฐ๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์ „ํˆฌํ•จ์€ ๋Œ€ํ˜•ํ™”, ํƒ‘์žฌ ๋ฌด์žฅ, ์žฅ๋น„์˜ ์ฆ๊ฐ€ ๋“ฑ์œผ๋กœ ์ธํ•ด ์ด์ „๋ณด๋‹ค ๋งŽ์€ ์šด์˜ ์ธ์›์ด ํ•„์š”ํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ์ ์ ˆํ•œ ์Šน์กฐ์›์˜ ์ˆ˜๋ฅผ ์ „ํˆฌํ•จ์— ๋ฐฐ์น˜ํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ๋˜ํ•œ ์ „ํˆฌํ•จ์€ ์—ฌ๋Ÿฌ ์šด์šฉ ์ƒํ™ฉ(์ „ํˆฌ, ์ •๋น„ ๋“ฑ)์„ ๊ณ ๋ คํ•ด์•ผ ํ•˜๊ณ  ์Šน์กฐ์›์˜ ํŠน๊ธฐ๊ฐ€ ๋‹ค์–‘ํ•˜๋ฏ€๋กœ ์Šน์กฐ์›์˜ ๊ตฌ์„ฑ์„ ์ „ํˆฌํ•จ์˜ ํŠน์„ฑ์— ๋งž๊ฒŒ ์ตœ์ ํ™”ํ•˜๋Š” ๊ฒƒ์ด ํ•„์ˆ˜์ ์ด๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ํ•ด๊ตฐ์€ ๊ด€๋ จ ๋…ธํ•˜์šฐ๋ฅผ ๊ฐ–์ถ˜ ์ „๋ฌธ๊ฐ€์™€ ์‹ค์ ์„  ๊ธฐ๋ฐ˜์˜ ์ž๋ฃŒ์— ์˜์กดํ•˜๊ณ  ์žˆ์œผ๋‚˜, ๊ตฐ ์ •์ฑ…์˜ ๋ณ€ํ™”, ์‹ ํ˜• ์ „ํˆฌํ•จ์˜ ๋Œ€ํ˜•ํ™”, ๋ฌด์žฅ์˜ ๋‹ค์–‘ํ™” ๋“ฑ์˜ ์ด์œ ๋กœ ์ถ”๊ฐ€์ ์ธ ์ตœ์ ํ™”๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์„ค๊ณ„ ํ•จ์ •์˜ ์ œ์›๊ณผ ์ฃผ์š” ํƒ‘์žฌ ์žฅ๋น„๊ฐ€ ์ฃผ์–ด์งˆ ๋•Œ, ํ˜„์žฌ ๊ตฐ์ด ์‹œํ–‰ ์ค‘์ธ ์‹ค์ ์„  ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•ด ์šด์˜ ๊ธฐ๋ฐ˜์˜ ์Šน์กฐ์› ๊ตฌ์„ฑ์„ ์ผ์ฐจ์ ์œผ๋กœ ์‚ฐ์ถœํ•˜์˜€๋‹ค. ํ•ด๋‹น ๊ฒฐ๊ณผ๋Š” ๊ณผ๊ฑฐ๊ธฐ๋ฐ˜์˜ ์Šน์กฐ์› ๊ตฌ์„ฑ์„ ์‚ฐ์ถœํ•˜์˜€๊ธฐ ๋•Œ๋ฌธ์— ์ถ”๊ฐ€์ ์œผ๋กœ ์ „๋ฌธ๊ฐ€์‹œ์Šคํ…œ์„ ํ™œ์šฉํ•˜์—ฌ ๋‚ด๊ฐ€ ์„ค๊ณ„ํ•˜๋Š” ํ•จ์ •์˜ ํŠน์„ฑ๊ณผ ํ˜„์žฌ ํ•จ์ • ์šด์˜์— ๋Œ€ํ•œ ์‚ฌํ•ญ์„ ๋ฐ˜์˜ํ•œ ๊ฒฐ๊ณผ๋ฅผ ์‚ฐ์ถœํ•˜์˜€๋‹ค. ์ดํ›„ ๊ทธ ๊ฒฐ๊ณผ๋ฅผ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ์ „ํˆฌํ•จ์˜ ์Šน์กฐ์› ๊ตฌ์„ฑ์„ ์ตœ์ ํ™”ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ์‹ค์ ์„  ์ž๋ฃŒ ๊ธฐ๋ฐ˜์˜ ์Šน์กฐ์› ์ถ”์ • ๋ฐฉ๋ฒ•์€ ๋‹ค์–‘ํ•œ ํŠน๊ธฐ๋ฅผ ๊ฐ€์ง„ ์Šน์กฐ์›์„ ํ•จ์ •์˜ ์ œ์›, ํƒ‘์žฌ๋œ ๋ฌด์žฅ ๋“ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ๋ถ„๋ฅ˜ํ•˜๊ณ , ํšŒ๊ท€ ๋ถ„์„ ๋“ฑ์„ ์ด์šฉํ•˜์—ฌ ์„ค๊ณ„ ํ•จ์ •์— ๋งž๋Š” ์Šน์กฐ์› ๊ตฌ์„ฑ์„ ์ถ”์ •ํ•˜๊ฒŒ ๋œ๋‹ค. ์ „๋ฌธ๊ฐ€ ์‹œ์Šคํ…œ ๊ธฐ๋ฐ˜์˜ ์Šน์กฐ์› ์ถ”์ • ๋ฐฉ๋ฒ•์€ Rule-based expert systems๋ฅผ ํ™œ์šฉํ•˜์—ฌ ํ•จ์ • ์šด์šฉ์„ ๊ณ ๋ คํ•˜์—ฌ ์„ค๊ณ„ํ•œ CEM(Crew manning Expert system Model)์„ ํ†ตํ•ด ์Šน์กฐ์› ๊ตฌ์„ฑ์„ ์žฌ์ถ”์ •ํ•˜๊ฒŒ๋œ๋‹ค. ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ๊ธฐ๋ฐ˜์˜ ์ตœ์ ํ™” ๋ฐฉ๋ฒ•์€ ํ•จ์ •์˜ ์‹ค์ œ ์šด์˜ ์ƒํ™ฉ์„ ๋ชจ์‚ฌํ•œ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ์ด์‚ฐ ์‚ฌ๊ฑด (DEVS: Discrete EVent System specification) ์‹œ๋ฎฌ๋ ˆ์ด์…˜์„ ์ด์šฉํ•ด ์ž„๋ฌด ์ˆ˜ํ–‰ ์‹œ๊ฐ„ ๋ฐ ํšจ์œจ์„ ๋น„๊ต ๋ถ„์„ํ•˜์—ฌ ์Šน์กฐ์› ๊ตฌ์„ฑ์„ ์ตœ์ ํ™”ํ•œ๋‹ค. ์ตœ์ข…์ ์œผ๋กœ ๊ฒ€์ฆ์„ ์œ„ํ•ด ์ž์ฒด ๊ฐœ๋ฐœ ํ”„๋กœ๊ทธ๋žจ์„ ๊ตฌํ˜„ํ•˜์˜€๊ณ , ๋ฏธ ํ•ด๊ตฐ์˜ ํ•จ์ •์˜ ์ œ์› ๋ฐ ์Šน์กฐ์›์˜ ์ˆ˜๋ฅผ ํ”„๋กœ๊ทธ๋žจ์— ์ž…๋ ฅํ•˜์—ฌ ์„ฑ๋Šฅ์„ ๊ฒ€์ฆํ•˜์˜€๋‹ค.Abstract 10 1. Introduction 12 1.1. Research background 12 1.2 Related works 14 1.3 Target of the study 16 2. The first estimation based on legacy ship data 20 2.1. Overview of the crew on board the naval ship 20 2.1.1. Boatswains Mate (BM) 20 2.1.2. Quartermasters (QM) 21 2.1.3. Information Technician (IT) 21 2.1.4. Operation Specialist (OS) 21 2.1.5. Electronic Warfare (EW) 21 2.1.6. Electronic Technicians (ET) 22 2.1.7. Fire Controlmen (FC) 22 2.1.8. Sonar Technician (ST) 22 2.1.9. Gunners Mate (GM) 22 2.1.10. Gasturbine System (GS) / Enginermen (EN) 22 2.1.11. Electricians Mate (EM) 23 2.1.12. Machinery Repairman (MR) 23 2.1.13. Culinary Specialist (CS) 23 2.1.14. Yeoman (YN) 23 2.1.15. Hospital Corpsman (HM) 23 2.1.16. Division of naval ship 24 2.2. Key consideration for estimation of crew manning 25 2.2.13. Analysis of the availability of navigation watch 27 2.2.14. Analysis of availability of crew deployment in a combat situation 28 2.2.15. Analysis considering the special task 30 2.3. System configuration of the first estimation 31 2.3.13. Input data of the first estimation 33 2.3.14. System configuration of the first estimation 34 2.3.15. Assignment of crew 34 2.3.16. Output data of the first estimation 36 3. The second estimation based on the Expert system 37 3.1. Knowledge representation 37 3.1.1. Production rule 37 3.1.2 Semantic net 38 3.1.3 Frame 40 3.1.4 Hybrid knowledge representation 41 3.2. Rule-based expert system 43 3.2.1. Knowledge base 43 3.2.2. Inference engine 44 3.2.3. User interface 44 3.3 Model using expert system 45 3.3.1. Object information 46 3.3.2. Relation information 48 3.3.3. Expert system for crew deployment 50 4. The final estimation using DEVS 51 4.1. System specification formalisms 51 4.2. DEVS formalism 52 4.2.1. Atomic model 53 4.2.2. Coupled model 57 4.3. Configuration of model 60 4.4. The first detailed DEVS model (For the naval ships combat situation) 62 4.4.1. Total scenario composition 63 4.4.2. Sub-scenario composition โ€“ AAW 64 4.4.3. Sub-scenario composition โ€“ Close ASUW 66 4.4.4. Sub-scenario composition โ€“ ASW 67 4.4.5. DEVS Model composition 68 4.5. The second detailed DEVS model (For the naval ships emergency situation) 72 4.5.1. Scenario composition 73 4.5.2. Composition of the DEVS model 76 5. User interface 78 5.1. Tool for estimation based on legacy ship data 79 5.2. Tool for estimation based on expert system 80 5.3. Tool for estimation based on DEVS 81 6. Application of the method for crew deployment 83 6.1. Description of an example 83 6.2. The first estimation based on legacy ship data for application 84 6.3. The second estimation based on experts knowledge for application 90 6.4. The final estimation based on DEVS for application 95 6.4.1. Result of DEVS model for a combat situation 96 6.4.2. Result of DEVS model for emergency situation 99 7. Conclusions and future works 102 References 104 APPENDIX 106 A. Detailed data of combat scenarios 107 ๊ตญ๋ฌธ ์ดˆ๋ก 109์„

    Optimization techniques with knowledge based control in ship concept design

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    This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.An integrated computational approach to Ship Concept Design using optimization techniques and a knowledge base to control the optimization process has been developed. The system automates both synthesis and analysis; analysis by the repeated sequential use of Design Theory Modules and synthesis through the optimization process, which compromises conflicting requirements, subject to constraints. The intention of this work has been to find a better approach to automated design synthesis and at the same time employ detailed analytical tools such as a three-dimensional hull-form definition and engineering analysis modules. Optimization techniques and a knowledge base are combined to achieve the desired capabilities, taking advantage of the benefits optimization can bring using goal oriented methods and exploratory searches, alongside a knowledge base that controls the synthesis process rather than the design. A function mapping strategy has been developed to provide a multiple-parametric view of regions of the optimization objective function and constraints. A discussion is included on the role of further applications of expert systems to design systems in both synthesis and analysis and their possible interference with creativity and innovation. Two design examples are provided, one showing the application of the system using optimization and the other adding the use of the knowledge base. The results are compared and discussed.National Council of Research (CNPq - Brazil

    Design of Large Diameter Mine Countermeasure Hybrid Power Unmanned Underwater Vehicle

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    Mines are one of the most cost-effective and moderated weapon systems that are easy to deploy, but difficult to clear. Not only has the development of the mine countermeasure (MCM) underwater unmanned vehicle (UUV) improved cost- and time-effectiveness in operation, but also it has avoided unnecessary human casualties

    Improving naval shipbuilding project efficiency through rework reduction

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    The rising cost of U.S. Naval Ships and the rate of change in technology require a thorough analysis of current shipbuilding practices. The Navy wants the latest and greatest technology, while at the same time keeping overall cost low. Some technologies are obsolete before completion of the ship's design and construction. A design locked in at Critical Design Review (CDR) undergoes multiple modifications prior to ship's delivery. Design changes drive up cost. The goal is to provide the Warfighter Battlespace Dominance while keeping cost low enough to allow a consistent purchase of additional ships. To accomplish this goal, both industry and the Navy must be aware of what is driving design changes and willing to revise existing practices. The objectives of this thesis are to identify the major sources of rework and to suggest modifications and improvements to existing practices. A review of DoD Acquisition and the Shipbuilding process identifies design changes resulting from requirements volatility, inconsistent execution of Defense Acquisition, and the rigidity of the design and construction process as major sources of rework. Recommendations include improving change management, optimizing the schedule for resilience, and the use of a modular open systems approach to reduce rework.http://archive.org/details/improvingnavalsh109453347Northrop Grumman Corporation authors (civilian).Approved for public release; distribution is unlimited

    Is a naval architect an atypical designer-or just a hull engineer?

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    Integrating XML and RDF Concepts to Achieve Automation Within a Tactical Knowledge Management Environment

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