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A Multi-Agent System Simulating Crew Interaction in a Military Aircraft. To appear

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A Multi-Agent System Simulating Crew Interactionin a Military Aircraft

Denis Gagné, Jocelyn Desbiens and Georges NaultGroupe de recherche en intelligence artificielle distribuée (GRIAD)Collège Militaire Royal de Saint-JeanComputer Sciences DepartmentRichelain, Québec, Canada, J0J 1R0

AbstractThe development of expert systems using parallel machines is a practice in its infancy, but its future is very promising.Researchers at the Artificial Intelligence Laboratory of the C.M.R. de Saint-Jean have been able to implement a cooperativesystem in which relatively independent agents cooperate to solve a specific problem. The system presented is implemented on adistributed memory parallel machine, enabling several autonomous expert systems executing simultaneously, to work together.This approach is part of a strategy to solve complex problems via a multi-agent distributed system.The problem dealt with--to simulate crew interaction in an anti-submarine aircraft--enabled us to demonstrate the manyadvantages to be gained, in a large number of applications, from the presented choice of software and hardware architecture.The long-term objective of this project is to provide a more productive and economical way of training crews of this aircraft.

Introduction

After years of enthusiasm followed by disenchantment, few areas of research in artificial intelligence still enable us to makeprogress without limitations; most techniques used have shown proof of intrinsic limitations or major weaknesses. Thus todate, only expert systems represent a real, functional success in limited applications. However, attempts to generalize theirexpertise or widen their areas of application have been, for the most part, doomed to failure.

Given the ambitions of AI systems, it is foreseeable that an increase in performance of over 10,000 times what is possible todaywill be required [Wah 88]. We already know that parallelizing the Rete algorithm (at the heart of most expert system shells)can bring about, at most, a 10 to 1 gain in performance [Gup 86]. Several parallel implementations of this algorithm haveshown that the possibility of using specific processors is limited. Theoretical analyses have shown that considerable improvedperformance can only be achieved in certain very specific cases [Bah 92].

Attempts at parallelization via the distribution of rules on separate processors have not been much more promising. In fact,where dependency relationships and intelligent distribution algorithms must be defined, it has been shown that even the bestapproach at distributing rules is at least a quadratic assignation problem (proven NP-complete) [Mol 89].

Our Choice of Paradigm

It appears that it will often be easier and more productive to execute a parallelization of the application or data (when this ispossible) rather than attempt to parallelize its resolution at the algorithmic level. It is therefore always worthwhile to exploreall parallelization possibilities at the application level even before continuing on to later stages.

It is further evident that an appreciable increase in the speed of execution will not be obtain unless we judiciously provide forfactors between two contradictory optimization options; the need to centralize tasks to minimize communications, and the needto distribute tasks as much as possible to increase the level of parallelism attained.

A known fact of system analysis is that most complex systems naturally tend to break themselves into relatively independentsubsystems. Within these subsystems, interaction between elements are frequent and rapid, while interaction between onesubsystem and another are comparatively rare and slow [Cou 77].

As a result of these and several other observations, we have chosen an approach which combines the strong points of twowidely used and proven paradigms, while making sure to avoid their disadvantages. An appreciable gain in performance,attributable to parallelism, is added to the power of expert systems applied to limited areas (in which they excel).

The thesis here is that most complex problems can better be solved via a pendemonium of micro agents, each of themspecializing in a different and very narrow aspect of the problem.

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东方航空英语试题及答案

东方航空英语试题及答案

东方航空英语试题及答案一、单选题（每题2分，共20分）1. What is the full form of "IATA"?A. International Airline Travel AssociationB. International Air Transport AssociationC. International Air Traffic AssociationD. International Aviation Travel Association答案：B2. Which of the following is not a function of an airline?A. TicketingB. CateringC. SecurityD. Manufacturing答案：D3. What does "ETD" stand for in airline terminology?A. Estimated Time of DepartureB. Estimated Time of ArrivalC. Estimated Time of DelayD. Estimated Time of Disembarkation答案：A4. Which of the following is not a type of aircraft?A. Boeing 737B. Airbus A380C. ConcordeD. Tesla Model S答案：D5. What is the meaning of "CRS" in the context of airlines?A. Customer Relationship ServiceB. Central Reservation SystemC. Customer Review SystemD. Crew Reporting System答案：B6. What does "FA" stand for in the aviation industry?A. Flight AttendantB. Flight AdministratorC. Flight AnalystD. Flight Assistant答案：A7. Which of the following is not a reason for a flight delay?A. Weather conditionsB. Technical issuesC. OverbookingD. Pilot strike答案：C8. What is the term for the process of loading and unloading an aircraft?A. Ground handlingB. Air handlingC. Cargo handlingD. Passenger handling答案：A9. What does "RFID" stand for in the airline industry?A. Radio Frequency IdentificationB. Rapid Flight IdentificationC. Real Flight IdentificationD. Remote Flight Identification答案：A10. Which of the following is not a standard safety procedure on a flight?A. Fastening seat beltsB. Turning off electronic devicesC. Smoking in the lavatoriesD. Following crew instructions答案：C二、填空题（每题2分，共20分）1. The ________ is responsible for coordinating the flight plan and ensuring the aircraft complies with air traffic control regulations.答案：pilot2. In the event of a medical emergency on board, the flight crew will typically request assistance from ________.答案：passengers3. The ________ is responsible for the safety and comfort of passengers during a flight.答案：flight attendant4. The ________ is the part of the airport where passengers board and disembark from aircraft.答案：gate5. The ________ is a system that allows passengers to check-in and print their boarding passes without the assistance of a human agent.答案：kiosk6. The ________ is a document that proves the holder has paid for a flight and has a right to travel on the specified flight.答案：boarding pass7. The ________ is the process of checking passengers and their luggage for prohibited items before boarding an aircraft.答案：security check8. The ________ is the area of an airport where aircraft are parked, refueled, and maintained.答案：apron9. The ________ is a device used to help passengers with reduced mobility move around the airport.答案：wheelchair10. The ________ is a system that provides passengers with real-time updates on flight status, gate changes, and delays. 答案：information display三、简答题（每题10分，共30分）1. Describe the role of an air traffic controller in the aviation industry.答案：An air traffic controller is responsible for managing the flow of aircraft in the sky and on the ground. They ensure that aircraft maintain safe distances from each other and follow the correct routes and altitudes. They also coordinate with pilots to provide information on weather conditions, air traffic, and other relevant factors to ensure safe and efficient flight operations.2. Explain the importance of regular aircraft maintenance.答案：Regular aircraft maintenance is crucial for ensuring the safety and reliability of air travel. It involves inspecting, servicing, and repairing aircraft components to prevent technical issues and potential accidents. Maintenance helps to extend the life of the aircraft, reduce operating costs, and uphold the standards set by aviation authorities.3. Discuss the significance of customer service in theairline industry.答案：Customer service is a key differentiator in the airline industry. It encompasses all interactions between passengers and airline staff, from ticket sales to in-flight service. Providing excellent customer service can lead to increased customer satisfaction, loyalty, and positive word-of-mouth. It also helps airlines to manage complaints and feedback effectively, which can lead to improvements in service delivery and overall business performance.。

GE-Aircraft-Engine-PD-Optimization

GE-Aircraft-Engine-PD-Optimization

Preliminary System Design Optimization of Aircraft EnginesK.Badhrinath, J. Madelone, S.Nagendra R.C.Plybon, M. WoodruffEngineering Mechanics Laboratory Propulsion System SimulationGeneral Electric – CR&D General Electric – Aircraft EnginesSchenectady, NY 12301 Cincinnati, OH 452151.0 IntroductionA preliminary study is performed to define an engine cycle, which meets the mission requirements for an aircraft application while limiting the cumulative time spent over the mission at life limiting engine temperatures. The other performance measures considered are the amount of fuel burnt, range of the mission and the cost of the engine. An objective function for determining the life of the engine was created based on combustor inlet temperature and turbine rotor entrance temperature. Air flow inlet area, cooling flows and the turbine flow sizes were varied to drive the performance measures. Reducing the time spent at high temperatures not only increases the potential life of the engine, but can also reduce cost and risk for the engine design. The amount of fuel burnt and the mission range are also important criteria for certain missions. Therefore, designing the engine which is optimal with respect to all these criteria is critical, in order to satisfy the commercial or military aircraft customers. In this paper we study various preliminary design optimization problems using the multidisciplinary design optimization framework called iSIGHT [1]. A different set of problems related to performance based preliminary design of airframe propulsion systems has been done previously by authors in [2].2.0 Analysis CodesThe physical behavior of an engine [4,5,6] is embodied in a set of data files and programs, which communicate in a certain fashion and mimic the behavior of an actual engine to a certain extent. This communication between various programs, also called analysis codes, and data files could become complicated depending on the level of details that we consider during the design. In this paper we consider the preliminary design of an engine. The three analysis codes used in this study are: (i) integrated cycle system (ICS), (ii) FLOWPATH, and (iii) Propulsion Evaluation Tool (PET2000). These analysis codes, which are used in GE Aircraft Engines to analyze various military and commercial aircraft engines at preliminary design level, are described below:2.0.1 Integrated Cycle SystemICS [7] allows the user to define an engine configuration and the performance of each component in the engine and then to evaluate engine performance at various operating conditions. The process of defining the component performance is called as Design Point calculation. Once defined, the process of evaluating the engine performance at other operating conditions is called an Off-Design calculation. The design point calculation is used to define an engine cycle that meets the basic requirements and limits of the application. The off design calculation is used to generate performance data needed for other design and analysis activities.2.0.2 FLOWPATHThe FLOWPATH program defines the geometry and weight of an engine based on technology and cycle information. It performs a simplified aerodynamic design of the various components that approximates the real design process to generate engine hardware geometry consistent with the cycle. Using this geometry information and mechanical/material technology inputs, FLOWPATH then performs a simplified mechanical analysis to generate overall geometry information and a weight and cost estimate. 2.0.3 Propulsion Evaluation Tool (PET2000)PET2000 [3] takes an aircraft definition, engine cycle data over a range of power settings and flight conditions and engine geometry and weight information and performs an analysis of the ability of that engine-aircraft combination to perform a specific mission.3.0 Analysis Codes IntegrationThe analysis model consists of a engine thermodynamic cycle model, an air flow path model, and an aircraft performance model. The cycle model is based on the GEAE Interactive Cycle System (ICS). This code produces the off-design cycle data required for FLOWPATH and PET2000. FLOWPATH is used to determine the engine weight and cost. This weight is used in the mission input file that is used by PET2000, which is the GEAE installed engine performance and aircraft evaluation tool is used for evaluating the engine-aircraft combination over the specified aircraft mission. ICS generates the raw uninstalled cycle data for the engine. This file and another input file which describes the aircraft and aircraft mission are provided to PET2000 which computes various engine performance parameters during each mission leg. It also provides aircraft related information such as net fuel burn, range and excess thrust which could be used in a more broadly focused study. The output from PET2000 which describes the temperature profile as a function of time is used to compute the objective function in this optimization problem. The analysis codes ICS, FLOWPATH, and PET2000, which are developed at the GE-Aircraft Engines are integrated in a multidisciplinary analysis and optimization software iSIGHT. A part of the information flow between the various analysis codes is shown in Figure 1.Figure 1: Flow of data in the ICS-FLOWPATH-PET2000 integration problem.This problem is set up in iSIGHT, using a main task and subtasks. The main tasks coordinates the subtasks which are cycle, flowpath and propulsion. For a given design, the main task runs these subtasks and extracts the required performance characteristics, which are then used in the optimization. The iSIGHT implementation of this problem is shown in Figure 2 and 3.SubTasks Figure 2: iSIGHT implementation of the ICS-FLOWPATH-PET2000 integration problem.Figure 3: A snapshot of hierarchy of tasks in the iSIGHT implementation of the ICS-FLOWPATH-PET2000 integration problem.3.0 Aircraft and the Mission ConsideredThe engine used for this study was a medium bypass augmented turbofan similar to those used in current military fighter/bomber applications. A generic supersonic aircraft model was created consistent with a single engine application for this nominal engine size. A representative fighter/bomber interdiction mission with high speed dash, maneuver and extended cruise legs was used. Although maneuver performance and excess power was available it was not considered. The selection of mission, aircraft and engine was made to demonstrate the full capability required for a typical study and to provide a representative template for the engine, aircraft and mission models that can be easily modified or replaced for future use.4.0 Optimization ProblemsThe criteria or objectives considered for optimization, in this study are life (which is based on the amount of time the temperatures at certain sections of the engines exceed an allowable value), range of the aircraft, cost, and the amount of fuel burnt during certain fixed legs of the mission. A constraint on HPturbine inlet temperature (T41) is also imposed on all the problems. The various optimization problem formulations are mentioned below.4.1 Problem ObjectivesFour distinct optimization problem sets were exercised using the ICSFLOWPATHPET iSIGHT application. Each problem focused on the optimization of a single output parameter.Table 1: Objectives and their bounds for various problemsSample Problems: Problem 1 Problem 2 Problem 3 Problem 4 Objectives: Direction Maximize Minimize Minimize MinimizeParameter MaxRange CMTSUM HotTime FuelBurnOutput Constraints: MaxRange [X originally]LB: 1350.0LB: 1350.0LB: 1350.0 LB: 1350.0 CMTSUM LB: 1350.0X UB: 2500.0 UB: 2500.0 FuelBurn UB: 4200.0UB: 4200.0UB: 4200.0 [X originally]UB: 4200.0 HotTime UB: 160.0UB: 160.0X UB: 160.0 T41 UB: 3200.0UB: 3200.0UB: 3200.0 UB: 3200.04.2 ConstraintsThere are 5 design (input) variables and 5 output parameters that were constrained during the execution of iSIGHT.Table 2: Bounds on variables and performance parametersData Parameter Sim.Code Lower Bound (LB) Upper Bound (UB) Inputs: SWR41 ICS 0.9 1.2SWR49 ICS 0.9 1.2G3W41 ICS 0.05 0.1G3W42 ICS 0.1 0.15WRD2A ICS 250 275 Outputs: MaxRange Pet2000 1350 XCMTSUMFlowpath X 2500FuelBurn Pet2000 X 4200HotTime Pet2000 X 160T41 Pet2000 X 3200Figure 4 – iSIGHT Representation of Parameter Constraints4.3 Problem DefinitionProblem 1 [Range Optimization Problem]Maximize Rangesuch thatCost ≤ 2500.0Overshoot Time ≤ 160.0Fuel Burn ≤ 4200.0T41 ≤ 3200.00.90 ≤ Inlet Flow Function Scalar (HPTR) [SWR41] ≤ 1.200.90 ≤ Inlet Flow Function scalar (LPTR) [SWR49] ≤ 1.200.05 ≤ HPT Non-Chargeable Cooling Flow Fraction [G3W41] ≤ 0.100.10 ≤ HPT Chargeable Cooling Flow Fraction [G3W42] ≤ 0.15250.0 ≤ Corrected flow [WRD2A] ≤ 275.0where the constraint, overshoot time, is the summation of all the time of the mission during which t avg > t ref .t avg = (t3 + t41)/2.0t ref = reference value of t avg where engine life impact becomes significant. All time between this temperatures and the maximum temperature is assumed to consume life limited parts.t3 = Temperature at the HP compressor exit.t41 = Temperature at the HP turbine rotor inlet.During the optimization process, the optimization variables defined in the above problem are changed in the input file to the ICS code, which computes the various cycle parameters. The off-design point cycle files corresponding to FLOWPATH and PET2000 are used for generating the thermodynamic cycle dataneeded while executing the respective codes. The aircraft engine weight is obtained from the FLOWPATH output for each design, which the optimizer generates. This information is used to update the total aircraft weight in the input file used by PET2000. In addition to that the inlet capture area in the installation file is scaled based on the inlet-corrected flow, which is one of the design variable.The overshoot time is computed by a separate c code, which extracts the required data from a PET2000 output file and performs the required computations. For this study a reference temperature (t ref) of 1900°F was chosen. This value was selected based on the cruise temperature (flat region) during the mission performed by an engine with a particular value of the input design variable vector. The maximum range of the mission is computed from the PET2000 output file using a Fortran code. The cost value is obtained from the FLOWPATH output file.Problem 2 [Cost Optimization Problem]Minimize Costsuch≥ 1350.0that RangeOvershoot Time ≤ 160.0Fuel Burn ≤ 4200.0T41 ≤ 3200.00.90 ≤ Inlet Flow Function Scalar (HPTR) [SWR41] ≤ 1.200.90 ≤ Inlet Flow Function scalar (LPTR) [SWR49] ≤ 1.200.05 ≤ HPT Non-Chargeable Cooling Flow Fraction [G3W41] ≤ 0.100.10 ≤ HPT Chargeable Cooling Flow Fraction [G3W42] ≤ 0.15250.0 ≤ Corrected flow [WRD2A] ≤ 275.0Problem 3 [Life Optimization Problem]Minimize Overshoot Timesuch≥ 1350.0that RangeCost ≤ 2500.0Fuel Burn ≤ 4200.0T41 ≤ 3200.00.90 ≤ Inlet Flow Function Scalar (HPTR) [SWR41] ≤ 1.200.90 ≤ Inlet Flow Function scalar (LPTR) [SWR49] ≤ 1.200.05 ≤ HPT Non-Chargeable Cooling Flow Fraction [G3W41] ≤ 0.100.10 ≤ HPT Chargeable Cooling Flow Fraction [G3W42] ≤ 0.15250.0 ≤ Corrected flow [WRD2A] ≤ 275.0Problem 4 [Fuel Burn Optimization Problem]Minimize Fuel Burn≥ 1350.0that RangesuchCost ≤ 2500.0Overshoot Time ≤ 160.0T41 ≤ 3200.00.90 ≤ Inlet Flow Function Scalar (HPTR) [SWR41] ≤ 1.200.90 ≤ Inlet Flow Function scalar (LPTR) [SWR49] ≤ 1.200.05 ≤ HPT Non-Chargeable Cooling Flow Fraction [G3W41] ≤ 0.100.10 ≤ HPT Chargeable Cooling Flow Fraction [G3W42] ≤ 0.15250.0 ≤ Corrected flow [WRD2A] ≤ 275.04.4 ResultsThe following is a summary of the best results obtained using the original constraint values. Many iterations of each problem were performed in order to determine the optimization strategy that produced the best results for the given problem. The flexibility of the system allows for modification of the optimization strategy of choice with a simple edit of the MDOL description file (i.e. selection of available techniques).Table 3: Results for the optimization problems defined in Section 4.3Results Summary Starting Point Problem 1 Maximize MaxRange Problem 2 Minimize Cost Problem 3 Minimize HotTime Problem 4MinimizeFuelBurnDesign VariablesSWR41 1 1.0631 1.0083 0.9688 0.9780SWR49 1.1 1.0423 1.0046 1.0197 1.0247G3W41 0.08 0.1000 0.1000 0.0847 0.0539G3W42 0.12 0.1002 0.1272 0.1402 0.1413WRD2A 261.6 261.5987 261.0095 252.6714 255.4872Performance ParametersMaxRange 1363.1 1367.3 1359.6 1350 1350.3CMTSUM 2554.1012499.807 2464.436 2471.739 2480.496FuelBurn 4203 4200 4199 4200 4194HotTime 156.2071156.8409 156.0291 154.409 154.4973T41 3134.0253110.55 3198.206 3173.732 3186.986Table 4: Analysis of design input variablesProblem Inputs SWR41 SWR49 G3W41 G3W42 WRD2A1 Initial 1 1.1 0.08 0.12 261.6 1 Best 1.063134855 1.042275255 0.1 0.100172696 261.59873521 Delta 0.0631 -0.0577 0.0200 -0.0198 -0.00131 % Delta 6.3135 -5.2477 25.0000 -16.5228 -0.00052 Initial 1 1.1 0.08 0.12 261.6 2 Best 1.008344603 1.004576304 0.1 0.127171546 261.00948812 Delta 0.0083 -0.0954 0.0200 0.0072 -0.59052 % Delta 0.8345 -8.6749 25.0000 5.9763 -0.22573 Initial 1 1.1 0.08 0.12 261.6 3 Best 0.968799372 1.019727716 0.084664922 0.140241883 252.67142393 Delta -0.0312 -0.0803 0.0047 0.0202 -8.92863 % Delta -3.1201 -7.2975 5.8312 16.8682 -3.41314 Initial 1 1.1 0.08 0.12 261.6 4 Best 0.978020451 1.024697274 0.053850037 0.141266718 255.48716474 Delta -0.0220 -0.0753 -0.0261 0.0213 -6.11284 % Delta -2.1980 -6.8457 -32.6875 17.7223 -2.3367Table 5: Analysis of design output variables (Green/Red) designate the objectivesProblem Outputs MaxRange CMTSUM FuelBurn HotTime T411 Initial 1363.12554.101 4203 156.2071 3134.025 1 Best 1367.32499.807 4200 156.8409 3110.55 1 Delta 4.2000 -54.2940 -3.0000 0.6338 -23.47501 % Delta 0.3081 -2.1258 -0.0714 0.4057 -0.74902 Initial 1363.1 2554.1014203 156.2071 3134.025 2 Best 1359.6 2464.4364199 156.0291 3198.206 2 Delta -3.5000 -82.3620-4.0000 -0.1780 64.1810 2 % Delta -0.2568 -3.2247-0.0952 -0.1140 2.0479 3 Initial1363.1 2554.101 4203 156.2071 3134.025 3 Best1350 2471.739 4200 154.409 3173.732 3 Delta-13.1000 -89.6650 -3.0000 -1.7981 39.7070 3 % Delta-0.9610 -3.5106 -0.0714 -1.1511 1.2670 4 Initial 1363.1 2554.101 4203156.2071 3134.025 4 Best 1350.3 2480.496 4194154.4973 3186.986 4 Delta-12.8000 -73.6050 -9.0000 -1.7098 52.9610 4 % Delta-0.9390 -2.8818 -0.2141 -1.0946 1.6899From the above table we could observe the trade-offs between the various performance parameters for designs that are optimal with respect to different criteria. Problems with various combinations of performance/cost objectives subjected to real life constraints and other advanced design optimization problem could be studied using the same framework.4.5 Result InterpretationThis problem is typical of those in the early phases of engine preliminary design process. It contains a wide range of potential independent variables but only a few which are considered for a specific study. Constraints may be specific to the study (range, fuel burn) or general technology limits associated with the validity of the underlying models. There are multiple constraints on both the dependent and independent variables, many of which have overlapping effects. These studies generally have a short life span. They must be generated quickly, examine the design space of interest, identify feasible regions and areas of opportunity and impact of key constraints. Quick insight into limiting assumptions is as often important as the final result. When doing engine only or an unconstrained study, the engineer can often analytically predict the areas of best performance and which constraints will be most limiting. As the studies become more complicated and involve the interaction between the constraints and optimum engine and aircraft behavior, finding the best engine and identifying which constraints are relevant becomes difficult.This study involved a highly constrained design space with a number of relatively flat response characteristics. The figures of merit varied from 4% to 0.2% over the design space and were sensitive to all design variables. Each engine optimum was limited by other dependent variable constraints. The optimization algorithms were able to identify these relatively small improvements as shown in Table 5.Although representative of preliminary engine design studies, the design variables and system goals will rarely stay the same from study to study. The goal of this effort was to not only perform a specific engine-aircraft analysis but to provide a template that can be easily modified for future engine-aircraft studies. With this framework, new design variables, figures of merit and associated constraints can be substituted in minutes.AcknowledgementsThe authors would like to acknowledge the assistance received from Don Elliott, Danny Cornett, and Randy Cepress of GE – Aircraft Engines.References[1] iSIGHT Developer’s Guide, Version 4.0, Engineous Software Inc.[2] Nagendra S., Plybon R.C., Performance based preliminary design of airframe propulsion systems in a collaborative optimization environment.[3] Woodruff M., GE Aircraft Engines Performance Evaluation Tool (PET2000) – Private Communication.[4] Oates G.C., Editor, Aircraft Propulsion Systems Technology and Design, AIAA Education Series, 1989.[5] Mattingly J.D., Heiser W.H., Daley D.H., Aircraft Engine Design, AIAA Education Series, 1987.[6] Hesse W.J., Mumford, JR., N.V.S., Jet Propulsion for Aerospace Applications, Second edition, Pitman Publishing Corp., 1964.[7] Interactive Cycle System (ICE 2.5.1) User’s Manual, Volume 1, Program Operation, TM90-237, GEAircraft Engines, 1996.。

基于Multi-Agent的人机环境仿真设计系统

基于Multi-Agent的人机环境仿真设计系统

基于Multi-Agent的人机环境仿真设计系统王朝晖;郑国磊;朱心雄【摘要】提出了用于制造过程仿真和人机工程分析的多Agent智能设计系统.基于模糊多目标决策理论建立了虚拟人自适应作业行为仿真模型.在虚拟环境中,智能化的虚拟人能自主完成作业任务的仿真,优化自身的工作状态,同时提供人机工程的评估结果.通过仿真实验,证明了所用技术的可行性.【期刊名称】《图学学报》【年(卷),期】2010(031)006【总页数】6页(P133-138)【关键词】虚拟人;模糊多目标决策;多Agent系统;自治行为【作者】王朝晖;郑国磊;朱心雄【作者单位】北京航空航天大学机械工程及自动化学院,北京,100191;北京航空航天大学机械工程及自动化学院,北京,100191;北京航空航天大学机械工程及自动化学院,北京,100191【正文语种】中文【中图分类】TP391.9产品设计必须满足人的需要，使人能够方便地制造、维护和使用产品。

“以人为中心”的设计思想认为，应将人机工程学应用到产品设计的每个阶段[1]。

目前，人机环境仿真与设计系统的研究得到了学术界和工业界的广泛重视。

一些仿真系统已成功地应用于工业设计、系统检测和人员训练等工作中[2-3]。

在此类系统中，虚拟人扮演了重要的角色，负责完成作业行为仿真，配合用户检查系统中存在的人因问题。

真实地展现人的作业行为有利于设计者理解人与产品的关系，找到影响人与产品行为交互的关键因素。

目前，行为仿真主要延续着“克隆”人体行为的技术思路，使用各种运动捕捉技术来获取人体的运动数据。

例如，电磁式运动捕捉技术和沉浸式虚拟现实技术相结合已应用于人体作业行为仿真与人机工程评估[4-5]。

在运动捕捉数据的基础上，人们开始关注行为组织原理及其仿真技术，分别从基于PaT-Net的行为同步控制[6]、行为数据的语义关系[7]和行为数据统计[8]等方向开展研究，以期实现自主性、灵活性更高且可预测的人体行为仿真。

船上人员落水演练方案及流程

船上人员落水演练方案及流程

船上人员落水演练方案及流程英文回答：As a maritime safety trainer, I have been involved in developing and implementing man overboard drills for ship crews. The purpose of these drills is to ensure that all crew members are familiar with the procedures for responding to a person falling overboard and are able to execute them effectively in case of an actual emergency.The first step in conducting a man overboard drill is to alert all crew members that the drill is about to take place. This can be done through the ship's alarm system or by making an announcement over the ship's intercom. It is important to simulate a realistic scenario, so the drill should be conducted at a random time when the crew is not expecting it.Once the crew has been alerted, the next step is to deploy a lifebuoy or other flotation device into the waterto simulate the person overboard. This should be done as quickly as possible to mimic a real-life situation where timing is crucial. The crew should be trained to keep their eyes on the person in the water at all times and to point and shout to alert others of the situation.After the person overboard has been identified and the alarm raised, the crew should then proceed to launch a rescue boat or deploy a rescue net to retrieve the person from the water. This requires coordination and communication among the crew members to ensure that the rescue operation is carried out safely and efficiently. It is important for the crew to practice these procedures regularly to maintain their proficiency and readiness.Following the successful retrieval of the person from the water, the crew should conduct a debriefing session to review the drill and identify any areas for improvement. This is an important step in the training process as it allows the crew to reflect on their performance and learn from any mistakes that were made. It also provides an opportunity to reinforce the importance of following properprocedures and working together as a team in emergency situations.In conclusion, conducting man overboard drills is a critical aspect of maritime safety training. By simulating realistic scenarios and practicing the necessary procedures, ship crews can ensure that they are prepared to respond effectively to emergencies at sea. Regular training and debriefing sessions are essential for maintaining thecrew's readiness and continuously improving their response capabilities.中文回答：作为海上安全培训师，我一直参与制定和实施船员落水演练。