A Methodology for Strategy Optimization Under Uncertainty

A Methodology for Strategy Optimization Under Uncertainty in the

Extended Two-Dimensional Pursuer/Evader Problem

Frank W. Moore and Dr. Oscar N. Garcia

Department of Computer Science and Engineering

303 Russ Engineering Center

Wright State University, Dayton, OH 45435

fmoore@https://www.sodocs.net/doc/0117956426.html,, ogarcia@https://www.sodocs.net/doc/0117956426.html,

ABSTRACT

To solve the extended two-dimensional pursuer/evader problem, a strategy must be identified by which an evader (such as an F-16C fighter aircraft) may maneuver to successfully evade pursuers (such as surface-to-air missiles) launched from a wide range of potentially lethal relative initial positions. Uncertainty about the type of pursuer introduces a degree of complexity that is difficult to model using traditional analytic or control-theoretic approaches. This paper describes the implementation of a genetic programming system that uses training populations reflecting specific probability distributions to evolve optimized solutions to the extended two-dimensional pursuer/evader problem under conditions of uncertainty about the type of pursuer.

1. Introduction

The two-dimensional pursuer/evader problem (Hamalainen and Ehtamo 1990) is a competitive zero-sum game in which a faster, more agile pursuer is given a limited amount of time to capture an evader as both are traveling across a plane. The game ends favorably for the evader if it manages to stay outside the lethal radius of the pursuer (the maximum distance at which a capture is considered to have occurred) for the duration of the encounter. A solution to the two-dimensional pursuer/evader problem must incorporate an optimized strategy for maneuvering the evader in a manner that successfully escapes the pursuer, regardless of the initial conditions of the system.

(Moore and Garcia 1997) described a genetic programming (GP) solution to the extended two-dimensional pursuer/evader problem(E2DPE). For this study, the system incorporated physical data (including mass) and performance characteristics (such as maximum thrust, maximum turning rate, fuel consumption rate, and drag coefficients) of an F-16C aircraft evader (Lambert and Munson 1994) and various types of Soviet surface-to-air missile (SAM) pursuers (Cullen and Foss 1995). Each best-of-run program was evolved using a training population of pursuers of a single SAM type, launched from a variety of potentially lethal positions. The resulting GP system was capable of evolving optimized programs that successfully evaded elements of the training population. Subsequent testing against pursuers of the same type demonstrated that the resulting best-of-run programs were also capable of evading a significantly high percentage of pursuers from a large, representative test population.

Best-of-run programs optimized against one type of pursuer generally do not perform optimally when tested against other types of pursuers. This research investigates the impact of uncertainty about the type of pursuer in the E2DPE problem. We are interested in determining a methodology for evolving programs that exhibit optimized performance against multiple pursuer types, by using training populations that reflect specific probability distributions over those types. The results of this investigation are summarized in this paper.

2. An Overview of the Extended Two-Dimensional Pursuer/Evader Problem As shown in Figure 1, E2DPE models pursuer P and evader E as point masses whose motions across a plane are controlled by thrusting forces (applied in the direction of the velocity vector) and turning forces (applied in a direction that is perpendicular to the velocity vector). Both P and E are affected by drag forces and momentum; instantaneous changes in direction

Predicted Impact Point

Figure 1. The Extended Two-Dimensional Pursuer/Evader Problem (Initial Conditions).

are not possible. Since acceleration (a), force (F), and mass (m) are related by the equation a = F/m, the acceleration of P or E depends on its current mass, as well as the magnitude of the applied thrusting and turning forces. The effects of these forces depend on the current state (the position, velocity, and acceleration vectors) of P or E. The maximum distance over which P pursues E depends on the type of pursuer. P captures E as soon as the distance between them becomes less than a pursuer-specific lethal radius.

Prior to the start of the encounter, the pursuer uses the initial state (position, velocity, and acceleration) of the evader to predict an intercept point. The pursuer is then launched at maximum thrust in the direction of the intercept point. If the evader fails to maneuver, the pursuer captures the evader at (or very close to) the intercept point. If the evader maneuvers, the pursuer relies upon the highly effective proportional navigation technique (Ball 1985) to pursue the evader. Proportional navigation causes the pursuer to accelerate in the direction perpendicular to the line-of-sight from the pursuer to the evader; the magnitude of this acceleration is calculated by the equation

n c = N’ V c (dλ/dt)

where N’ is a unitless designer-chosen gain known as the effective navigation ratio, and V c is the pursuer-evader closing velocity vector (the negative rate of change of the distance from the pursuer to the evader). The time derivative of the line-of-sight angle λ is known as the line-of-sight rate. For practical guidance systems, optimal values for N’ range between 3 and 5(Ramo and Pucket 1959); for this study, each pursuer used an effective navigation ratio N’ = 4.

The evader maneuvers by executing specific combinations of thrusting and turning forces in specific sequences. The optimal strategy for the evader is to maneuver in a manner that maximizes the likelihood of evading the pursuer, regardless of the initial state of the evader and the relative launch position of the pursuer (Zarchan 1990). Note that by rotating the reference coordinate system at the launch site of the pursuer, the initial pursuer/evader line-of-sight angle λ0 may be considered constant for all pursuer/evader pairs. For this reason, the only variables necessary to describe the initial configuration of each confrontation are the line-of-sight distance between the evader and the pursuer, and the velocity vector of the evader at the time the pursuer is launched.

For each pursuer type, the minimum and maximum effective range of the pursuer defines the range of possible initial line-of-sight distances. The aggregate fitness of a specific program reflects its fitness when executed against each of these initial pursuer positions. The optimal evasion program considers the current state of the evader and pursuer, and outputs commands that assert thrusting and turning forces at appropriate moments in order to accomplish maneuvers that optimize evader survivability:

THRUST -- Set the thrusting force to

the specified percentage of the

evader’s maximum thrust.

TURN – Apply a turning force equal to

the specified percentage of the

X

evader’s maximum turning force,

in a direction perpendicular to the

evader’s current velocity vector;

negative values indicate a left turn,

while positive values indicate a

right turn.

3. Prior Research

(Moore and Garcia 1997) described a genetic programming (Koza 1992) solution to the E2DPE problem. During each generation of the genetic programming approach, each member of a population of programs for maneuvering the evader was trained against each member of a training population of pursuers. Each genetic programming run optimized maneuvers against a single type of pursuer. Proportional navigation was used by all pursuers. The pursuer and evader had complete knowledge of each other’s current state (relative position, velocity, and acceleration). A fitness function was used to qualitatively evaluate each evasion program during a simulated encounter; the aggregate fitness of a particular program reflected its fitness when independently trained against all of the pursuers in the training population. Fitness-proportionate reproduction, together with crossover, was used to create each new generation. Each run was terminated after a fixed number of generations.

A tableau for the E2DPE problem is shown Figure 2. Cartesian coordinates were used to represent components of position, velocity, and acceleration for both the evader and the pursuer. This simple set of terminals proved to be sufficient to allow the genetic programming system to converge to a solution of this problem. This two-dimensional pursuer/evader problem fixes the origin of the coordinate system (x = 0, y = 0) at the position of the pursuer. PX and PY thus continually designate the relative displacement from the pursuer to the evader.

For each of the functions used by this GP system, an “argument” may consist of any syntactically valid composition of functions, variables, and constants that returns a floating-point value in the range [-1.0 … +1.0]. Function ifPX is a two-argument selection function: if the x-displacement of the evader relative to the position of the pursuer is negative, then the first argument is evaluated; otherwise, the second argument is evaluated. Function ifPY is a two-argument selection function defined in a similar manner for the y-displacement of the evader. Function ifDistance is a three-argument selection function: if the current distance between the pursuer and evader is less than the percentage of the maximum pursuit distance specified by the absolute value of the first argument, then the second argument is evaluated; otherwise the third argument is evaluated. Functions ifPX, ifPY, and ifDistance each return the value of the evaluated argument. Functions setThrust and hardTurn are single-argument functions. Function setThrust causes the thrust output of the evader to be set to the percentage of its maximum thrust specified by its argument; for example, setThrust (0.9) will set evader thrust to 90% of its maximum possible value. SetThrust ignores the sign of its argument; thrust always acts in the direction of the current evader velocity vector. Function hardTurn causes the evader to execute a turn whose g-force equals the percentage of the maximum allowable turning force of the evader/pilot system specified by its argument; for example, if the maximum turning force is 4 gravities (g’s), then hardTurn (0.5) will cause the evader to execute a 2g turn in a direction which is perpendicular and to the right of the current evader velocity vector; the function call hardTurn (-0.5) would result in a 2g evader turn to the left. Both setThrust and hardTurn are assumed to act instantaneously, and both return the value of their input argument.

The following example illustrates a program that might be automatically created by the GP system used for this project:

(ifDistance 0.1

(hardTurn (setThrust -0.75))

(setThrust (hardTurn 0.9)))

This program will cause the evader to thrust at 90% of its maximum thrust value, and turn to the right at 90% of its maximum turning rate,

Objective: Determine an optimized evasion strategy for the extended

two-dimensional pursuer/evader problem.

Terminal Set:PX, the displacement in the x-direction from the pursuer to the evader.

PY, the displacement in the y-direction from the pursuer to the evader.

R, the ephemeral random floating-point constant ranging from -1.0 to 1.0. Function Set: ifPX

ifPY

ifDistance

setThrust

hardTurn

Fitness Cases: Numerous fitness cases which differ according to the distance from the

pursuer to the evader at the start of the encounter, as well as the acute

angle between the initial velocity vector of the evader and the line-of-sight

vector from evader to pursuer.

Raw Fitness:The number of times the distance between the pursuer and evader is less

than or equal to the lethal envelope of the pursuer.

Standardized Fitness:Same as Raw Fitness for this problem.

Hits:The number of fitness cases that result in capture of the evader prior to the maximum pursuit time of the pursuer. (The encounter is also terminated if

the distance between the evader and the pursuer exceeds a pursuer-specific

value, at which time the pursuer is considered to have missed the evader.) Wrapper:N/A

Parameters:Population Size M = 100, Maximum Number of Generations G = 21. Success Predicate:None.

Figure 2. A Tableau for the Extended Two-Dimensional Pursuer/Evader Problem

until the pursuer has closed to within 10% of the maximum pursuit range; it will then cause the evader to turn left at 75% of its maximum turning rate, and set thrust to 75% of its maximum thrust value.

Fitness cases were identified by two values. The first value, denoted J, identifies the initial line-of-sight distance from the pursuer to the evader. If D min and D max denote the minimum and maximum effective launch distances for the pursuer, then the initial line-of-sight distance d0 may be calculated as follows:

d0 = D min + (J * (D max - D min))

D min and D max depend upon the type of pursuer. The second value, denoted K, identifies the angle that the initial velocity vector of the incoming evader makes with the line-of-sight from the evader to the pursuer. Let Θ0denote this angle. If Θmin and Θmax denote the minimum and maximum initial value of Θ, then Θ0 may be calculated in the following manner:Θ0 = Θmin + (K * (Θmax - Θmin))

To maintain the relative geometry illustrated in Figure 1 for the pursuer/evader problems addressed by this research, Θmin and Θmax described a range of values between 10 and 80 degrees. For this study, the magnitude of the evader’s velocity vector (its “speed”) at pursuer launch time was assumed to be the same for all encounters. Each fitness case corresponded to a specific combination of J and K.

The research described in (Moore and Garcia 1997) identified a methodology for evolving optimized evasion strategies (in the form of programs) for a variety of pursuer populations. We began with the two-dimensional problem to reduce the required amount of computation as much as possible. Implicit in the model described above is the assumption that the magnitude of the turning force is independent of the velocity of the evader. Additionally, limitations on the maximum sustainable g-force of the evader were imposed by restricting the magnitude of the evader’s turning force; the contribution of thrust to current evader g-force was ignored. We also assumed that the evader stalled if its speed fell below a specified minimum value, resulting in a “kill” for the pursuer. For the purposes of this study, all evaders were assumed to be traveling inbound(towards the pursuer) at the start of each confrontation, with lead angle γ0>λ0, as shown in Figure 1. The best-of-run program represented an optimized evasion technique for a specific type of evader and a specific type of pursuer.

The evader used in (Moore and Garcia 1997) was modeled using physical data and performance characteristics of an F-16C aircraft evader. Pursuers were modeled using physical

data and performance characteristics of an SA-6, SA-13, or SA-15 surface-to-air missile (SAM) pursuer. Programs were evolved using a training population consisting of a single type of pursuer, launched from a variety of potentially lethal positions. Fifteen separate training runs evolved best-of-run programs for each type of pursuer. As shown in Figure 3, these runs differed in the training population of pursuers (described by J and K), as well as the random number seed used during the creation and subsequent evolution of the program population.

The best-of-run programs produced in Training Runs 1-10 were capable of evading 100% of the pursuers in the training population. Because small values for both J and K were used in Training Runs 11-15, presenting these programs with more difficult situations than encountered during Training Runs 1-10, Training Runs 11-15 required significantly greater computational resources to converge to an optimized solution to the E2DPE problem . 4. Introducing Uncertainty About the Type of Pursuer Uncertainty introduces a degree of complexity into the E2DPE problem that is difficult to model using traditional analytical and control-theoretic approaches (Shinar and Steinberg 1977; Zarchan 1990). This study is concerned with determining a methodology for using genetic programming to evolve programs that exhibit optimized performance against unknown or uncertain pursuer types. For this study, each of the best-of-run programs evolved for a single type of pursuer (SAM) was subsequently tested against three test populations. Each test population consisted of 128 pursuers of a single type (SA-6, SA-13, or SA-15), described by the following values for J and K:

J ∈ {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8}

K ∈ {0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45,

0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85} The results of these tests are tabulated in Figure 4. Each value represents the number of pursuers successfully evaded by the corresponding best-of-run program. As illustrated by Figure 4, the best-of-run programs optimized against one type of pursuer generally do not perform optimally when tested against other types of pursuers.

SA-6 Test 10127 (99.2%)113 (88.3%)102 (79.7%)342 (89.1%)

SA-6 Test 11125 (97.7%)114 (89.1%)105 (82.0%)344 (89.6%)

SA-6 Test 12128 (100%)115 (89.8%)104 (81.3%)347 (90.1%)

SA-6 Test 13128 (100%)110 (85.9%)89 (69.5%)327 (85.2%)

SA-6 Test 14127 (99.2%)109 (85.2%)92 (71.9%)328 (85.4%)

SA-6 Test 15128 (100%)112 (87.5%)109 (85.2%)349 (90.9%)

SA-13 Test 1127 (99.2%)115 (89.8%)102 (79.7%)344 (89.6%)

SA-13 Test 2127 (99.2%)113 (88.3%)110 (85.9%)350 (91.1%)

SA-13 Test 3127 (99.2%)113 (88.3%)104 (81.3%)344 (89.6%)

SA-13 Test 4126 (98.4%)113 (88.3%)112 (87.5%)352 (91.7%)

SA-13 Test 596 (75.0%)113 (88.3%)29 (22.7%)238 (62.0%)

SA-13 Test 699 (77.3%)116 (90.6%)29 (22.7%)244 (63.5%)

SA-13 Test 7127 (99.2%)113 (88.3%)110 (85.9%)350 (91.1%)

SA-13 Test 892 (71.9%)110 (85.9%)23 (18.0%)225 (58.6%)

SA-13 Test 9126 (98.4%)113 (88.3%)112 (87.5%)351 (91.4%)

SA-13 Test 1095 (74.2%)113 (88.3%)23 (18.0%)231 (60.2%)

SA-13 Test 11126 (98.4%)116 (90.6%)92 (71.9%)334 (87.0%)

SA-13 Test 12128 (100%)115 (89.8%)105 (82.0%)348 (90.6%)

SA-13 Test 1393 (72.7%)120 (93.8%)105 (82.0%)318 (82.8%)

SA-13 Test 14104 (81.3%)115 (89.8%)89 (69.5%)308 (80.2%)

SA-13 Test 15112 (87.5%)120 (93.8%)94 (73.4%)326 (84.9%)

SA-15 Test 1125 (97.7%)113 (88.3%)110 (85.9%)348 (90.6%)

SA-15 Test 2127 (99.2%)113 (88.3%)109 (85.2%)349 (90.9%)

SA-15 Test 3128 (100%)112 (87.5%)106 (82.8%)346 (90.1%)

SA-15 Test 4128 (100%)116 (90.6%)100 (78.1%)344 (89.6%)

SA-15 Test 5127 (99.2%)113 (88.3%)111 (86.7%)351 (91.4%)

SA-15 Test 6128 (100%)113 (88.3%)114 (89.1%)355 (92.4%)

SA-15 Test 7128 (100%)110 (85.9%)90 (70.3%)328 (85.4%)

SA-15 Test 8128 (100%)112 (87.5%)106 (82.8%)346 (90.1%)

SA-15 Test 987 (68.0%)110 (85.9%)87 (68.0%)284 (74.0%)

SA-15 Test 10127 (99.2%)113 (88.3%)111 (86.7%)351 (91.4%)

SA-15 Test 11124 (96.7%)112 (87.5%)111 (86.7%)348 (90.6%)

SA-15 Test 12124 (96.7%)113 (88.3%)113 (88.3%)350 (91.1%)

SA-15 Test 13128 (100%)113 (88.3%)112 (87.5%)353 (91.9%)

SA-15 Test 14107 (83.6%)115 (89.8%)113 (88.3%)335 (87.2%)

SA-15 Test 15124 (96.7%)115 (89.8%)114 (89.1%)353 (91.9%) Figure 4. Results of Testing Best-of-Run Programs Against Different Types of Pursuers

Clearly, the most difficult type of pursuer to evade in these tests was the SA-15. Programs trained against SA-15s were only 2% less effective than programs optimized against SA-13s, when subsequently tested against a large SA-13 population; and actually outperformed programs trained against SA-6s, when subsequently tested against a large SA-6 population. In contrast, programs trained against SA-6s and SA-13s generally did poorly against the SA-15 test population. We attribute the robustness of the SA-15 programs to the fact that the pursuers from the SA-15 training population presented a more challenging problem for the GP system to solve during program evolution. Simply put, E2DPE programs generally perform better during testing when they are evolved against more challenging training populations. This observation brings forward the critical question addressed by this paper:

Can the use of a training population reflecting a specific probability distribution over possible pursuer types help evolve programs that perform near-optimally against an unknown or uncertain type of pursuer?

To begin to answer this question, a new set of fifteen best-of-run programs were evolved under conditions analogous to those described in (Moore and Garcia 1997). Instead of using a training population consisting of a single type of pursuer, however, the new set of programs were evolved against pursuers that were equally likely

to be an SA-6, SA-13, or SA-15. Each of the resulting best-of-run programs was subsequently tested against three large, representative test populations (one for each type of pursuer) described by sets of J and K values that were identical to those used in previous tests. The results of these tests are summarized in Figure 5. The aggregate scores of programs evolved against SA-6s, SA-13, SA-15s, and all three types of pursuers are tabulated in Figure 6.

5. Analysis of Test Results

The results this study demonstrate that our GP system was capable of evolving programs that exhibited optimized survivability against multiple SAM types. The use of training populations reflecting particular probability distributions over possible pursuer types helped evolve programs that exhibited better aggregate performance than programs evolved against a single type of pursuer, when subsequently tested against large, representative test populations reflecting similar distributions over pursuer type. In addition, programs evolved against multiple pursuer types actually out-performed programs evolved against a single type of pursuer, when subsequently tested against that type of pursuer. We attribute improved program performance to the increased number and types of challenges created by introducing multiple

Figure 5. Test Results for Programs Optimized Against All Three Pursuer Types Figure 6. Aggregate Test Results vs. Pursuers of Each Type

pursuer types in the training population.

The best-of-run programs evolved by genetic programming systems frequently exhibit optimal (or near-optimal) performance in competitive survival environments explicitly represented by the training population used to evolve the program. Unfortunately, the subsequent performance of these programs is often less than optimal when situations arise that were not explicitly anticipated during program evolution. The training sets used to optimize evasion programs under conditions of uncertainty about the type of pursuer included both SA-15s (the most challenging type of pursuer used in this study) and SA-8s (whose limited range introduced several short-distance, small-angle fitness cases into the training set, thus presenting more challenging scenarios for the GP system). We believe that the added difficulty of defeating multiple pursuer types during program evolution resulted in best-of-run

programs that exhibited better fitness with less

brittleness than their counterparts evolved against single pursuer types, when subsequently tested against large, representative populations of multiple pursuer types.

6. Conclusions

The E2DPE problem is significantly more complex than other pursuer/evader problems described in the available literature. The GP system developed for this study was capable of automatically producing evasion programs that exhibited near-optimal performance against large, representative test populations comprised of different types of pursuers. These results suggest that the use of multiple types of pursuers during program evolution may allow GP to evolve programs that exhibit near-optimal performance in competitive survival environments where the type of pursuer (i.e., its performance capabilities) is unknown or uncertain. As part of ongoing dissertation research, the system described in this paper is being extended to investigate methods of using GP to optimize missile countermeasures under conditions of uncertainty about the state of the SAM, and to include the use of electronic countermeasures such as chaff, flares, and jamming. This research will ultimately lead to a GP solution to the three-dimensional missile countermeasures optimization problem. References

Ball, R. E., 1985. The Fundamentals of Aircraft Combat Survivability Analysis and Design, AIAA Education Series, AIAA Inc.

Cullen, T. and C. Foss, 1995. Jane’s Land-Based Air Defence: 1995-1996, Jane’s Information Group, Inc.

Hamalainen, R. P. and H. K. Ehtamo (eds.), 1990. Differential Games -- Developments in Modeling and Computation, Lecture Notes in Control and Information Sciences Vol. 156, Springer-Verlag.

Koza, J. R., 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press.

Krasovskii, N. N. and A. I. Subbotin, 1988. Game-Theoretical Control Problems, https://www.sodocs.net/doc/0117956426.html,mbert, M. and K. Munson (eds.), 1994. Jane’s All the World’s Aircraft: 1994-1995, Jane’s Information Group, Inc.

Moore, F. W. and O. N. Garcia, 1997. “A Genetic Programming Approach to Strategy Optimization in the Extended Two-Dimensional Pursuer/Evader Problem”, in Proceedings of the Second Annual Conference on Genetic Programming, MIT Press.

Ramo, S. and A. Pucket, 1959. Guided Missile Engineering, McGraw-Hill, pp. 176-180.

Shinar, J. and D. Steinberg, 1977. “Analysis of Optimal Evasive Maneuvers Based on a Linearized Two-Dimensional Model”, in Journal of Aircraft, Vol. 14, August 1977, pp. 795-802.

Zarchan, P., 1990. Tactical and Strategic Missile Guidance, Progress in Astronautics and Aeronautics Vol. 124, American Institute of Aeronautics and Astronautics.

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英语选修六课文翻译第五单元word版本

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英语选修六课文翻译第五单元 reading An exciting job I have the greatest job in the world. travel to unusual places and work alongside people from all over the world sometimes working outdoors sometimes in an office sometimes using scientific equipment and sometimes meeting local people and tourists I am never bored although my job is occasionally dangerous I don't mind because danger excites me and makes me feel alive However the most important thing about my job is that I heIp protect ordinary people from one of the most powerful forces on earth-the volcano. I was appointed as a volcanologist working for the Hawaiian Volcano Observatory (HVO) twenty years ago My job is collecting information for a database about Mount KiLauea which is one of the most active volcanoes in Hawaii Having collected and evaluated the information I help oyher scientists to predict where lava from the path of the lava can be warned to leave their houses Unfortunately we cannot move their homes out of the way and many houses have been covered with lava or burned to the ground. When boiling rock erupts from a volcano and crashes back to earth, it causes less damage than you might imagine. This is because no one lives near the top of Mount Kilauea, where the rocks fall. The lava that flows slowly like a wave down the mountain causes far more damage because it buries everything in its path under the molten rock. However, the eruption itself is really exciting to watch and I shall never forget my first sight of one. It was in the second week after I arrived in Hawaii. Having worked hard all day, I went to bed early. I was fast asleep when suddenly my bed began shaking and I heard a strange sound, like a railway train passing my window. Having experienced quite a few earthquakes in Hawaii already, I didn't take much notice. I was about to go back to sleep when suddenly my bedroom became as bright as day. I ran out of the house into the back garden where I could see Mount Kilauea in the distance. There had been an eruption from the side of the mountain and red hot lava was fountaining hundreds of metres into the air. It was an absolutely fantastic sight. The day after this eruption I was lucky enough to have a much closer look at it. Two other scientists and I were driven up the mountain and dropped as close as possible to the crater that had been formed duing the eruption. Having earlier collected special clothes from the observatory, we put them on before we went any closer. All three of us looked like spacemen. We had white protective suits that covered our whole body, helmets,big boots and special gloves. It was not easy to walk in these suits, but we slowly made our way to the edge of the crater and looked down into the red, boiling centre. The other two climbed down into the crater to collect some lava for later study, but this being my first experience, I stayed at the top and watched them.

人教版英语选修六Unit5 the power of nature(An exciting Job)

高二英语教学设计 Book6 Unit 5 Reading An Exciting Job 1.教学目标（Teaching Goals）: a. To know how to read some words and phrases. b. To grasp and remember the detailed information of the reading material . c. To understand the general idea of the passage. d. To develop some basic reading skills. 2.教学重难点: a.. To understand the general idea of the passage. b. To develop some basic reading skills. Step I Lead-in and Pre-reading Let’s share a movie T: What’s happened in the movie? S: A volcano was erupting. All of them felt frightened/surprised/astonished/scared…… T: What do you think of volcano eruption and what can we do about it? S: A volcano eruption can do great damage to human beings. It seems that we human beings are powerless in front of these natural forces. But it can be predicted and damage can be reduced. T: Who will do this kind of job and what do you think of the job? S: volcanologist. It’s dangerous. T: I think it’s exciting. Ok, this class, let’s learn An Exciting Job. At first, I want to show you the goals of this class Step ⅡPre-reading Let the students take out their papers and check them in groups, and then write their answers on the blackboard (Self-learning) some words and phrases：volcano, erupt, alongside, appoint, equipment, volcanologist, database, evaluate, excite, fantastic, fountain, absolutely, unfortunately, potential, be compared with..., protect...from..., be appointed as, burn to the ground, be about to do sth., make one’s way. Check their answers and then let them lead the reading. Step III Fast-reading 这是一篇记叙文，一位火山学家的自述。作者首先介绍了他的工作性质，说明他热爱该项工作的主要原因是能帮助人们免遭火山袭击。然后，作者介绍了和另外二位科学家一道来到火山口的经历。最后，作者表达了他对自己工作的热情。许多年后，火山对他的吸引力依然不减。 Skimming Ⅰ.Read the passage and answer: (Group4) 1. Does the writer like his job?( Yes.) 2. Where is Mount Kilauea? (It is in Hawaii) 3. What is the volcanologist wearing when getting close to the crater? (He is wearing white protective suits that covered his whole body, helmets, big boots and

Unit5 Reading An Exciting Job(说课稿)

Unit5 Reading An Exciting Job 说课稿 Liu Baowei Part 1 My understanding of this lesson The analysis of the teaching material:This lesson is a reading passage. It plays a very important part in the English teaching of this unit. It tells us the writer’s exciting job as a volcanologist. From studying the passage, students can know the basic knowledge of volcano, and enjoy the occupation as a volcanologist. So here are my teaching goals: volcanologist 1. Ability goal: Enable the students to learn about the powerful natural force-volcano and the work as a volcanologist. 2. Learning ability goal: Help the students learn how to analyze the way the writer describes his exciting job. 3. Emotional goal: Make the Students love the nature and love their jobs. Learn how to express fear and anxiety Teaching important points: sentence structures 1. I was about to go back to sleep when suddenly my bedroom became as bright as day. 2. Having studied volcanoes now for more than twenty years, I am still amazed at their beauty as well as their potential to cause great damage. Teaching difficult points: 1. Use your own words to retell the text. 2. Discuss the natural disasters and their love to future jobs. Something about the S tudents: 1. The Students have known something about volcano but they don’t know the detailed information. 2. They are lack of vocabulary. 3. They don’t often use English to express themselves and communicate with others.

an exciting job 翻译

我的工作是世界上最伟大的工作。我跑的地方是稀罕奇特的地方，我见到的是世界各地有趣味的人们，有时在室外工作，有时在办公室里，有时工作中要用科学仪器，有时要会见当地百姓和旅游人士。但是我从不感到厌烦。虽然我的工作偶尔也有危险，但是我并不在乎，因为危险能激励我，使我感到有活力。然而，最重要的是，通过我的工作能保护人们免遭世界最大的自然威力之一，也就是火山的威胁。 我是一名火山学家，在夏威夷火山观测站（HVO）工作。我的主要任务是收集有关基拉韦厄火山的信息，这是夏威夷最活跃的火山之一。收集和评估了这些信息之后，我就帮助其他科学家一起预测下次火山熔岩将往何处流，流速是多少。我们的工作拯救了许多人的生命，因为熔岩要流经之地，老百姓都可以得到离开家园的通知。遗憾的是，我们不可能把他们的家搬离岩浆流过的地方，因此，许多房屋被熔岩淹没，或者焚烧殆尽。当滚烫沸腾的岩石从火山喷发出来并撞回地面时，它所造成的损失比想象的要小些，这是因为在岩石下落的基拉韦厄火山顶附近无人居住。而顺着山坡下流的火山熔岩造成的损失却大得多，这是因为火山岩浆所流经的地方，一切东西都被掩埋在熔岩下面了。然而火山喷发本身的确是很壮观的，我永远也忘不了我第一次看见火山喷发时的情景。那是在我到达夏威夷后的第二个星期。那天辛辛苦苦地干了一整天，我很早就上床睡觉。我在熟睡中突然感到床铺在摇晃，接着我听到一阵奇怪的声音，就好像一列火车从我的窗外行驶一样。因为我在夏威夷曾经经历过多次地震，所以对这种声音我并不在意。我刚要再睡，突然我的卧室亮如白昼。我赶紧跑出房间，来到后花园，在那儿我能远远地看见基拉韦厄火山。在山坡上，火山爆发了，红色发烫的岩浆像喷泉一样，朝天上喷射达几百米高。真是绝妙的奇景！ 就在这次火山喷发的第二天，我有幸做了一次近距离的观察。我和另外两位科学被送到山顶，在离火山爆发期间形成的火山口最靠近的地方才下车。早先从观测站出发时，就带了一些特制的安全服，于是我们穿上安全服再走近火山口。我们三个人看上去就像宇航员一样，我们都穿着白色的防护服遮住全身，戴上了头盔和特别的手套，还穿了一双大靴子。穿着这些衣服走起路来实在不容易，但我们还是缓缓往火山口的边缘走去，并且向下看到了红红的沸腾的中心。另外，两人攀下火山口，去收集供日后研究用的岩浆，我是第一次经历这样的事，所以留在山顶上观察他们

英语选修六课文翻译第五单元

英语选修六课文翻译第五单元 reading An exciting job I have the greatest job in the world. travel to unusual places and work alongside people from all over the world sometimes working outdoors sometimes in an office sometimes using scientific equipment and sometimes meeting local people and tourists I am never bored although my job is occasionally dangerous I don't mind because danger excites me and makes me feel alive However the most important thing about my job is that I heIp protect ordinary people from one of the most powerful forces on earth-the volcano. I was appointed as a volcanologist working for the Hawaiian Volcano Observatory (HVO) twenty years ago My job is collecting information for a database about Mount KiLauea which is one of the most active volcanoes in Hawaii Having collected and evaluated the information I help oyher scientists to predict where lava from the path of the lava can be warned to leave their houses

新目标英语七年级下册课文Unit04

新目标英语七年级下册课文Unit04 Coverstion1 A: What does your father do? B: He＇s a reporter. A:Really?That sounds interesting. Coverstion2 A:What does your mother do,Ken? B:She＇s a doctor. A:Really?I want to be a doctor. Coverstion3 A:What does your cousin do? B:You mean my cousin,Mike? A:Yeah,Mike.What does he do? B:He is as shop assistant. 2a,2b Coverstion1 A: Anna,doesyour mother work? B:Yes,she does .She has a new job. A:what does she do? B: Well ,she is as bank clerk,but she wants to be a policewoman. Coverstion2 A:Is that your father here ,Tony?

B:No,he isn't .He's working. B:But it's Saturday night.What does he do? B:He's a waiter Saturday is busy for him. A:Does he like it? B:Yes ,but he really wants to be an actor. Coverstion3 A:Susan,Is that your brother? B:Yes.it is A:What does he do? B:He's a student.He wants to be a doctor. section B , 2a,2b Jenny:So,Betty.what does yor father do? Betty: He's a policeman. Jenny:Do you want to be a policewoman? Betty:Oh,yes.Sometimes it's a little dangerous ,but it's also an exciting job.Jenny, your father is a bank clerk ,right? Jenny:Yes ,he is . Sam:Do you want to be a bank clerk,too? Jenny:No,not really.I want to be a reporter. Sam: Oh,yeah?Why? Jenny:It's very busy,but it's also fun.You meet so many interesting people.What about your father ,Sam. Sam: He's a reporter at the TV station.It's an exciting job,but it's also very difficult.He always has a lot of new things to learn.Iwant to be a reporter ,too

高中英语_An exciting job教学设计学情分析教材分析课后反思

人教版选修Unit 5 The power of nature阅读课教学设计 Learning aims Knowledge aims: Master the useful words and expressions related to the text. Ability aims: Learn about some disasters that are caused by natural forces, how people feel in dangerous situations. Emotion aims: Learn the ways in which humans protect themselves from natural disasters. Step1 Leading-in 1.Where would you like to spend your holiday? 2.What about a volcano? 3.What about doing such dangerous work as part of your job ? https://www.sodocs.net/doc/0117956426.html, every part of a volcano. Ash cloud/volcanic ash/ Crater/ Lava /Magma chamber 【设计意图】通过图片激发学生兴趣，引出本单元的话题，要求学生通过讨论, 了解火山基本信息，引出火山文化背景，为后面的阅读做铺垫。利用头脑风暴法收集学生对课文内容的预测并板书下来。文内容进行预测，培养学生预测阅读内容的能力。同时通过预测激起进一步探究 Step2. Skimming What’s the main topic of the article?

新目标英语七年级下课文原文unit1-6

Unit1 SectionA 1a Canada, France ,Japan ,the United States ,Australia, Singapore ,the United Kingdom,China 1b Boy1:Where is you pen pal from,Mike? Boy2:He's from Canada. Boy1:Really?My pen pal's from Australia.How about you,Lily?Where's your pen pal from? Girl1:She's from Japan.Where is Tony's pen pal from? Gril2:I think she's from Singapore. 2b 2c Conversation1 A: Where's you pen pal from, John? B: He's from Japan, A:Oh,really?Where does he live? B: Tokyo. Conversation2 A: Where's your pen pal from, Jodie? B: She's from France. A: Oh, she lives in Pairs. Conversation3 A: Andrew, where's your pen pal from? B: She's from France. A: Uh-huh. Where does she live? B: Oh, She lives in Paris.