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    Sustainable seabed mining: guidelines and a new concept for Atlantis II Deep

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    The feasibility of exploiting seabed resources is subject to the engineering solutions, and economic prospects. Due to rising metal prices, predicted mineral scarcities and unequal allocations of resources in the world, vast research programmes on the exploration and exploitation of seabed minerals are presented in 1970s. Very few studies have been published after the 1980s, when predictions were not fulfilled. The attention grew back in the last decade with marine mineral mining being in research and commercial focus again and the first seabed mining license for massive sulphides being granted in Papua New Guineaโ€™s Exclusive Economic Zone.Research on seabed exploitation and seabed mining is a complex transdisciplinary field that demands for further attention and development. Since the field links engineering, economics, environmental, legal and supply chain research, it demands for research from a systems point of view. This implies the application of a holistic sustainability framework of to analyse the feasibility of engineering systems. The research at hand aims to close this gap by developing such a framework and providing a review of seabed resources. Based on this review it identifies a significant potential for massive sulphides in inactive hydrothermal vents and sediments to solve global resource scarcities. The research aims to provide background on seabed exploitation and to apply a holistic systems engineering approach to develop general guidelines for sustainable seabed mining of polymetallic sulphides and a new concept and solutions for the Atlantis II Deep deposit in the Red Sea.The research methodology will start with acquiring a broader academic and industrial view on sustainable seabed mining through an online survey and expert interviews on seabed mining. In addition, the Nautilus Minerals case is reviewed for lessons learned and identification of challenges. Thereafter, a new concept for Atlantis II Deep is developed that based on a site specific assessment.The research undertaken in this study provides a new perspective regarding sustainable seabed mining. The main contributions of this research are the development of extensive guidelines for key issues in sustainable seabed mining as well as a new concept for seabed mining involving engineering systems, environmental risk mitigation, economic feasibility, logistics and legal aspects

    ์ „ํ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„

    Assessing the effectiveness of direct gesture interaction for a safety critical maritime application

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    Multi-touch interaction, in particular multi-touch gesture interaction, is widely believed to give a more natural interaction style. We investigated the utility of multi-touch interaction in the safety critical domain of maritime dynamic positioning (DP) vessels. We conducted initial paper prototyping with domain experts to gain an insight into natural gestures; we then conducted observational studies aboard a DP vessel during operational duties and two rounds of formal evaluation of prototypes - the second on a motion platform ship simulator. Despite following a careful user-centred design process, the final results show that traditional touch-screen button and menu interaction was quicker and less erroneous than gestures. Furthermore, the moving environment accentuated this difference and we observed initial use problems and handedness asymmetries on some multi-touch gestures. On the positive side, our results showed that users were able to suspend gestural interaction more naturally, thus improving situational awareness

    A feasibility study for the provision of electronic healthcare tools and services in areas of Greece, Cyprus and Italy

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    Background: Through this paper, we present the initial steps for the creation of an integrated platform for the provision of a series of eHealth tools and services to both citizens and travelers in isolated areas of thesoutheast Mediterranean, and on board ships travelling across it. The platform was created through an INTERREG IIIB ARCHIMED project called INTERMED. Methods: The support of primary healthcare, home care and the continuous education of physicians are the three major issues that the proposed platform is trying to facilitate. The proposed system is based on state-of-the-art telemedicine systems and is able to provide the following healthcare services: i) Telecollaboration and teleconsultation services between remotely located healthcare providers, ii) telemedicine services in emergencies, iii) home telecare services for "at risk" citizens such as the elderly and patients with chronic diseases, and iv) eLearning services for the continuous training through seminars of both healthcare personnel (physicians, nurses etc) and persons supporting "at risk" citizens. These systems support data transmission over simple phone lines, internet connections, integrated services digital network/digital subscriber lines, satellite links, mobile networks (GPRS/3G), and wireless local area networks. The data corresponds, among others, to voice, vital biosignals, still medical images, video, and data used by eLearning applications. The proposed platform comprises several systems, each supporting different services. These were integrated using a common data storage and exchange scheme in order to achieve system interoperability in terms of software, language and national characteristics. Results: The platform has been installed and evaluated in different rural and urban sites in Greece, Cyprus and Italy. The evaluation was mainly related to technical issues and user satisfaction. The selected sites are, among others, rural health centers, ambulances, homes of "at-risk" citizens, and a ferry. Conclusions: The results proved the functionality and utilization of the platform in various rural places in Greece, Cyprus and Italy. However, further actions are needed to enable the local healthcare systems and the different population groups to be familiarized with, and use in their everyday lives, mature technological solutions for the provision of healthcare services

    Automated Predictive Diagnosis (APD): A 3-tiered shell for building expert systems for automated predictions and decision making

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    The APD software features include: On-line help, Three level architecture, (Logic environments, Setup/Application environment, Data environment), Explanation capability, and File handling. The kinds of experimentation and record keeping that leads to effective expert systems is facilitated by: (1) a library of inferencing modules (in the logic environment); (2) an explanation capability which reveals logic strategies to users; (3) automated file naming conventions; (4) an information retrieval system; and (5) on-line help. These aid with effective use of knowledge, debugging and experimentation. Since the APD software anticipates the logical rules becoming complicated, it is embedded in a production system language (CLIPS) to insure the full power of the production system paradigm of CLIPS and availability of the procedural language C. The development is discussed of the APD software and three example applications: toy, experimental, and operational prototype for submarine maintenance predictions

    AI and OR in management of operations: history and trends

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    The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

    The Application of Integrated Knowledge-based Systems for the Biomedical Risk Assessment Intelligent Network (BRAIN)

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    One of NASA's goals for long duration space flight is to maintain acceptable levels of crew health, safety, and performance. One way of meeting this goal is through the Biomedical Risk Assessment Intelligent Network (BRAIN), an integrated network of both human and computer elements. The BRAIN will function as an advisor to flight surgeons by assessing the risk of in-flight biomedical problems and recommending appropriate countermeasures. This paper describes the joint effort among various NASA elements to develop BRAIN and an Infectious Disease Risk Assessment (IDRA) prototype. The implementation of this effort addresses the technological aspects of the following: (1) knowledge acquisition; (2) integration of IDRA components; (3) use of expert systems to automate the biomedical prediction process; (4) development of a user-friendly interface; and (5) integration of the IDRA prototype and Exercise Countermeasures Intelligent System (ExerCISys). Because the C Language, CLIPS (the C Language Integrated Production System), and the X-Window System were portable and easily integrated, they were chosen as the tools for the initial IDRA prototype. The feasibility was tested by developing an IDRA prototype that predicts the individual risk of influenza. The application of knowledge-based systems to risk assessment is of great market value to the medical technology industry
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