Edinburgh EH14 1DJ STATE ASSIGNMENT OF FINITE STATE MACHINES USING A GENETIC ALGORITHM

STATE ASSIGNMENT OF FINITE STATE MACHINES USING A GENETIC ALGORITHM

A.E.A Almaini, J.F. Miller, P. Thomson and S. Billina.

Department of

Electrical, Electronic and Computer Engineering

Napier University

219 Colinton Road

Edinburgh EH14 1DJ

STATE ASSIGNMENT OF FINITE STATE MACHINES

USING A GENETIC ALGORITHM

Indexing terms: State Assignment, Finite State Machines, Genetic Algorithm.

Abstract

The use of Genetic Algorithms for the generation of optimal state assignments for synchronous finite state machines (FSM) is proposed. Results are presented to show that in all examples attempted the resulting state assignments are better than or at least as good as those produced by SPECTRAL, NOVA and MUSTANG and also closed partition assignments. On average the genetic algorithm produced assignments with 33% less logic than the best produced by other algorithms.

1. Introduction

Attempts at solving the state assignment problem spreads over five decades. Amongst the early attempts are those due to Armstrong [1], Dolotta and McCluskey [2] and the decomposition technique due to Hartmanis and Stearns [3,4]. The state assignment refers to the allocation of unique binary codes to the states of sequential switching circuits. The discussion is limited to the state assignment of synchronous sequential machines that employ minimum number of state variables. It is known that all valid assignment codes produce workable circuits. The problem is that the resulting circuits may vary vastly in terms of their complexity and there is no known method of predicting the optimum assignment for the states. Exhaustive search is only possible for relatively small state machines, of the order of eight states, say. The partitioning technique developed by Hartmanis and Stearns [3,4] is arguably the most systematic study of the subject. One of its drawbacks is that not all state machines have well behaved closed partitions.

Recent work has tended to look for assignments that can be easily automated and give good though not necessarily optimum results. Some of these are now incorporated into commercial ECAD tools and may be targeted at two levels [5] or multi-level [6] implementations.

This paper describes a technique which tackles the problem in an alternative way to the analytical approach of partitioning, or the heuristics of commercially available software. Using a genetic algorithm, all possible assignments for a given problem may be considered as a search space of potential solutions. The genetic algorithm, then, essentially searches this space in a systematic manner whilst employing various methods for the avoidance of settling upon localised optima. It is by this structured search that a good solution for the state assignment may be found.

2. Genetic Algorithms

Genetic Algorithms (GAs) [7,8] present a non-deterministic approach to problem solving. The technique originated in Holland's study of adaptation in natural and artificial systems has now been successfully applied to a large number of varied NP-hard problems.

The basic method is to create a population of randomly selected potential solutions to the problem. These solutions are encoded as 'chromosomes' (abstract representations of problem solutions), and each chromosome is subjected to an evaluation function that assigns a 'fitness' depending upon how well the solution it encodes solves the problem at hand.

The chromosomes are recombined by a process called 'crossover'. The rationale behind this is that good solutions will contain good building blocks (partial solutions), the re-arrangement of which may produce even better solutions. Further, a 'mutation' process makes random changes to a few randomly selected 'genes' (partial solutions) within chromosomes, preventing premature convergence by maintaining the genetic diversity of the population. The population is then iterated through many generations and each individual's survival depends upon its fitness; hence, the best genes tend to be preserved, and the average fitness of the population should increase from generation to generation.

The mechanism for the selection of parent chromosomes is crucial as this is the driving force behind the gradual increase in average fitness of each generation as the GA evolves. A well known way to achieve this has been the so called 'roulette wheel selection'. Also known in statistics as the 'Monte Carlo' method, this is simply an algorithm for selecting parent chromosomes in proportion to their fitness. This means that particularly fit chromosomes will be chosen as parent chromosomes a number of times. They will then be able to 'pass on' genetic sub-features to a number of offspring.

A GA has many parameters to be fine-tuned for the problem at hand, and it is still a matter of some debate as to what these settings should be, and how they should be established. The parameters in question are: (i) the population size, (ii) the crossover rate: the number of chromosomes which will breed in one generation, and (iii) the mutation rate: the percentage of genes in the population that will be altered.

The mechanisms for parent selection and crossover within GAs are very much a research topic in their own right, and many alternatives have been developed [9]. The most effective choice of these mechanisms may be problem related as indeed are the particular values of the GA parameters.

3. A Genetic Algorithm for the State Assignment Problem

An FSM with m states requires a minimum of s state variables for the assignment

where s =

2g . The number of logically unique [10] assignments for an FSM, N , is given by:

N m s s s =??()!()!!

212The chromosome representation chosen was a 'pick list'. For example, the 'pick list' 34 2 1 1 would map the states 0 1 2 3 4 to the assignment 2 4 1 0 3. This comes about as follows: pick the third state, which gives 2, then remove this from the list. Next, pick the fourth state, which gives 4, then remove. This procedure is then continued for all five states.The reason for this apparently complicated chromosome representation is that it avoids the generation of invalid assignments which may otherwise have arisen by (i) random selection in the initial population and (ii) the processes of crossover and mutation. If the assignment itself were used as the chromosome, then the duplication of code assignments to particular states would be unavoidable. This would hamper the functioning of the genetic search because each chromosome would have to be repaired to re-establish their validity as problem solutions.

The genetic algorithm's search space is given by N above. The fitness f c i () of a chromosome c i in a generation having an ESPRESSO multi-output minimised literal count l i was given by f c l l i j i ()max{}=? , where max{}l j is the maximum literal count found in the generation. For each chromosome a Berkeley standard PLA file is created, this is then passed to ESPRESSO for minimisation. It is the literal count for this minimisation that forms the fitness for that chromosome. Parent chromosomes were randomly selected with a frequency proportional to their relative fitness (windowed roulette wheel selection). Pairs were then formed ready for crossover in such a way as to avoid the production of duplicate children.Once the new generation is created, elitism is employed to promote the best parent from the previous generation into the new generation. The algorithm used is described in figure 3.1.

Step 1. Create num_chromosomes randomly generated chromosomes c i ;

Step 2. Evaluate the fitness of each chromosome f c i ();

Step 3. best_chromosome =c i such that f c i ()=max{f c j ()};

Step 4. num_generations =1;

Step 5. num_children=(percent_breeding*num_chromosomes);

Step 6. while (max_num_generations not exceeded) do

begin

select num_children parent chromosomes by windowed roulette wheel;

apply one-point crossover to create num_children new chromosomes;

replace num_children parents with new chromosomes (children);

mutate percent_mutation genes of total genes;

replace randomly selected chromosome with best_chromosome (elitism);

evaluate the fitness of new chromosomes f c j ();

if (f c j ()> max{f c i ()})

best_chromosome =c j ;

num_generations=num_generations+1;

end;

Figure 3.1 The Genetic Algorithm for State Assignment.

From experience the following GA parameters were chosen: population size = 30,breeding rate = 40%, minimum mutation rate = 8%. The mutation rate was variable and increased with each generation if there had been no improvement in the literal count of the best chromosome. It then dropped back to 8% if improvement had occurred. The number of generations over which the GA was run was a maximum of 500. However it was observed that for machines with less than 9 states, the number of generations required for the GA to operate effectively was lower at somewhere between 50 and 100.

4. The Closed Partitions State Assignment

The existence of closed partitions on the states of a FSM gives an insight into the structure of the circuit. If the state assignment code is based on closed partitions then the circuit is decomposed into smaller units operating in parallel, serial or as a composite structure. In fact the structure can be predicted by simple inspection of the lattice diagram of the partitions. The reduced dependency between the state variables after decomposition normally results in simpler logic equations. It should be pointed out that not all state machines have closed partitions though in theory they can be made to do so by state splitting.It is felt that a comparison between the performances of the genetic algorithm and those obtained by industry standard algorithm, namely NOVA, MUSTANG and SPECTRAL should give a clear indication of the potential of this algorithm. A comparison with the partitions assignments should give a better understanding of the algorithm and its capability of finding what are known to be good solutions. First, however, a brief review of the relevant aspects of partition theory are given.A partition, π, on the states of a sequential machine is called a closed partition with respect to the machine, if for any two states S i and S j belonging to the same block of π and any input x ,the next states S x i and S x j are also in a common block of π. The existence of a partition τ and a closed partition π on the set of states of a sequential machine such that πτπ.()=0 is a sufficient condition for the machine to be decomposed into two components operating in

#(2ππ.

#(2τbetween the blocks of τ. This can be generalised to a cascade of more than two components

[3,4,11]. Similarly, if there exist n closed partitions such that ππππ120....()n =, the machine can be decomposed into n components operating in parallel and independent of each other. If ππππ12+++≠...()n I , then a component may be factored out to produce a composite structure.

5. Results

Due to space limitations, only selected examples are considered in detail. In every case the logic was minimised using the multi-output logic minimiser, ESPRESSO. The codes for the state assignments are given in Table 5.3. The KISS2 files for the examples which are not benchmarks are given in Table 5.4. These were taken from [11 - 13].

The partition assignment for example 'dk27' resulted in a predictable serial decomposition as shown in fig 5.1. The GA which is biased towards minimising the number of literals, generated an assignment which required the smallest number of literals. This bias can be adjusted , if desired, to favour the minimisation of the number of cubes or the number of AND/OR gates. The other algorithms produced comparable assignments.

The example 'test' had three useful closed partitions and resulted in two components in parallel with a predecessor as can be predicted from the lattice diagram in Fig 5.2. The parallel components are independent of each other. The GA produced the best result with the partition assignment a close second, both much better than all the others.

The example 'swma2' assignment was based on three partitions of the same block size, one of which is also an input consistent partition [11]. The result is probably the best possible, three components operating in parallel independently of each other one of which is independent of the external primary input. The GA solution is comparable but with more dependence. MUSTANG produced the next best solution followed by NOVA and SPECTRAL.

π12135647

=;,;,;,π21256247

=,,,;,,Partition equations:

GA equations:

Y y x 220

=Y y y x y y y x 21002100=++Y y y x y y y x y x 1210212010=+++Y y x y y y y 1202101=++Y y y x y y x y x 010010020

=++Y y y x y y y y 01002102

=++NOVA equations:

MUSTANG equations:

Y y y x y y y y 220021010=++Y y x 210

=Y y y x y y y x 12001010=++Y y y y y x 121200

=+Y y x y y 0100100

=+Y y y x y y x y y 020021010

=++SPECTRAL equations:

Y y y x y y y y 22101020=++Y y x 110

=Y y y x y y x y y x y x 020*********

=+++Figure 5.1. Equations for benchmark dk27

π

1

12346857

=,;,;,;,

π

2

18374526

=,;,;,;,

π

3

12683457

=,,,;,,,

Partition equations:GA equations:

Y y

22

=Y y

22

=

Y y y x y x y x y y

1210102021

=+++Y y y x

1020

=+

Y y y y y

02020

=+Y y y x y x y y

02101021

=++

NOVA equations:MUSTANG equations:

Y y y x y y x y y

221020021

=++Y y

22

=

Y y y x y y x y y x y y y

1210200100210

=+++

Y y y y x y y

y x

1210020

100

=++

Y y y x y y x y y x

021*******

=++

Y y y y x y y y x

y y x y y x y y

021002100

10010021

=++

++

SPECTRAL equations:

Y y y y y y y y

22101021

=++

Y y y x y x y y x y y

11002010021

=+++

Y y y x y y x y y x y y x

0100200200200

=+++

Figure 5.2. Equations for example 'test'

πτ114682357==,,,;,,,i π212345678=,,,;,,,π312783456

=,,,;,,,Partition equations:GA equations:Y y x 220

=Y y x y x 22020=+Y y x y x 11010=+Y y 11

=Y y 00

=Y y x y x 00020

=+NOVA equations:MUSTANG equations:Y y y x y y x y x 221010000=++Y y x 220=Y y 11

=Y y 11

=Y y y x y y x y x 021010020

=++Y y y x y x y y 02000020

=++SPECTRAL equations:

Y y y y x y y x y y x y y x 22100210100200=+++Y y y x y x 120010

=+Y y y y x y y x y y y y 021*********

=+++Figure 5.3. Equations for example 'swma2'

The following results (tables 5.1 and 5.2) show the performance, in terms of the number of two input gates required, for the various state assignment algorithms considered. The tables show, for each algorithm, a breakdown of the number of literals and the number of two input AND gates and OR gates respectively.

Table 5.1. Comparison of algorithms (including output logic)

Table 5.2. Comparison of algorithms (ignoring output logic)

donfile: GA: 2, 3, 0, 4, 1, 5, 6, 7, 10, 11, 8, 9, 12, 13, 14, 15, 21, 23, 17, 16, 18, 19, 25, 27

NOVA:0, 2, 25, 9, 3, 11, 5, 1, 21, 22, 4, 6, 16, 27, 7, 31, 26, 15, 18, 8, 13, 12, 10, 14

Mu: 6, 7, 14, 22, 10, 12, 2, 18, 3, 11, 19, 15, 13, 4, 5, 21, 20, 29, 0, 1, 16, 8, 17, 9

Sp:0, 4, 6, 8, 10, 11, 1, 5, 14, 16, 18, 19, 2, 9, 12, 17, 22, 23, 3, 7, 13, 15, 20, 21

bbtas: GA: 7, 1, 6, 0, 4, 5NOVA: 2, 3, 1, 0, 5, 6 Mu: 6, 0, 5, 1, 3, 2 Sp: 0, 1, 2, 3, 4, 5

dk14:GA: 5, 7, 1, 3, 6, 0, 4 NOVA: 1, 4, 0, 2, 7, 5, 3 Mu: 0, 1, 2, 6, 3, 4, 7 Sp: 0, 5, 1, 4, 2, 3, 6

dk15:GA: 2, 3, 0, 1NOVA: 1, 0, 3, 2Mu: 0, 1, 3, 2Sp: 0, 1, 2, 3

dk16:GA:17, 29, 0, 1, 28, 25, 5, 4, 15, 7, 9, 26, 16, 21, 23, 14, 8, 31, 11, 27, 20, 13, 6, 3, 12, 30, 2

NOVA:12, 7, 1, 3, 4, 10, 24, 23, 5, 27, 15, 16, 11, 6, 0, 20, 31, 2, 13, 25, 21, 14, 18, 19, 30, 17, 22

Mu:6, 4, 3, 18, 10, 8, 5, 12, 19, 14, 24, 13, 0, 1, 3, 22, 27, 7, 9, 11, 25, 23, 16, 20, 21, 17, 15

Sp:0, 5, 1, 8, 9, 10, 11, 6, 7, 2, 3, 4, 21, 22, 19, 12, 13, 16, 17, 18, 14, 15, 23, 24, 25, 20, 26

dk27:GA: 5, 1, 2, 3, 6, 7, 0NOVA: 2, 6, 5, 3, 4, 0, 7Mu: 2, 3, 7, 4, 1, 0, 5 Sp: 0, 3, 5, 2, 4, 1, 6 Pa: 0, 6, 1, 4, 2, 3, 5

dk512:GA:4, 3, 11, 15, 14, 9, 12, 7, 2, 1, 0, 10, 13, 8NOVA:1, 10, 8, 0, 3, 7, 15, 6, 14, 12, 13, 9, 4, 11 Mu:9, 11, 1, 0, 4, 3, 5, 2, 14, 10, 12, 6, 7, 8 Sp:0, 3, 5, 4, 6, 7, 12, 1, 2, 8, 9, 10, 11,13

Pa:0, 8, 1, 2, 3, 9, 10, 4, 11, 12, 13, 5, 6, 7

modulo12: GA:11, 14, 4, 3, 10, 12, 7, 2, 8, 15, 6, 0NOVA:0, 15, 1, 14, 2, 13, 3, 12, 4, 11, 5, 10

Mu:0, 1, 9, 8, 2, 3, 4, 5, 7, 15, 6, 14Sp:0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11

Pa:0, 10, 5, 12, 2, 9, 4, 14, 1, 8, 6, 13

shiftreg:GA:0, 1, 6, 2, 7, 3, 4, 5NOVA: 0, 4, 3, 2, 5, 1, 6, 7Mu: 0, 2, 6, 4, 1, 3, 7, 5

Sp: 0, 2, 3, 5, 1, 4, 6, 7Pa: 0, 1, 2, 3, 4, 5, 6, 7

tav:GA: 3, 1, 0, 2NOVA: 0, 3, 1, 2 Mu: 0, 1, 2, 3 Sp: 0, 1, 2, 3 Pa: 0, 2, 1, 3

test:GA: 0, 2, 6, 7, 5, 3, 4, 1NOVA: 7, 0, 6, 1, 5, 2, 4, 3

Mu: 6, 4, 0, 1, 2, 5, 3, 7Sp: 0, 3, 1, 2, 6, 4, 7, 5

Pa: 0, 2, 7, 5, 4, 3, 6, 1

table1:GA:0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 10, 11, 14, 15NOVA:15, 4, 10, 13, 0, 3, 14, 1, 8, 7, 9, 6, 11, 5, 2, 12 Mu:4, 0, 3, 7, 1, 5, 6, 2, 10, 8, 9, 11, 12, 13, 14, 15 Sp:0, 3, 6, 9, 10, 7, 4, 0, 14, 12, 13, 15, 8, 11, 2, 5 Pa:0, 8, 4, 12, 1, 9, 5, 13, 2, 10, 6, 14, 3, 11, 7, 15

table2:GA: 4, 11, 8, 5, 7, 9, 15, 12, 13NOVA: 10, 12, 1, 14, 5, 2, 13, 0, 3

Mu: 7, 6, 4, 15, 3, 2, 5, 0, 1Sp:0, 2, 1, 3, 4, 5, 8, 6, 7

Pa: 0, 1, 4, 6, 9, 10, 13, 12, 14

table3:GA: 6, 3, 2, 0, 4, 1NOVA: 6, 0, 2, 5, 4, 7 Mu: 4, 1, 3, 5, 2, 0 Sp: 0, 4, 1, 2, 5, 3 Pa: 0, 3, 2, 6, 4, 7

swma1:GA: 2, 5, 0, 7, 1, 6, 4, 3 NOVA: 0, 4, 5, 3, 2, 6, 1, 7 Mu: 0, 7, 2, 6, 1, 3, 5, 4 Sp: 0, 2, 4, 6, 1, 5, 7, 3 Pa: 2, 0, 3, 4, 6, 7, 1, 5

swma2:GA: 7, 6, 2, 4, 5, 0, 3, 1NOVA: 0, 3, 6, 5, 2, 4, 7, 1 Mu: 0, 3, 2, 1, 7, 4, 6, 5 Sp: 0, 3, 1, 4, 7, 6, 2, 5 Pa: 0, 1, 3, 2, 7, 6, 5, 4

swma3:GA: 6, 15, 14, 10, 8, 0, 2, 4, 12, 7, 9, 11, 3, 1, 5, 13NOVA:10, 12, 13, 9, 3, 1, 5, 15, 14, 7, 0, 4, 6, 2, 11, 8 Mu:5, 12, 15, 11, 8, 3, 0, 14, 13, 9, 1, 2, 7, 4, 10, 6 Sp:0, 3, 4, 5, 9, 11, 1, 12, 13, 2, 6, 7, 10, 8, 14, 15 Pa:0, 10, 4, 14, 12, 1, 2, 11, 15, 8, 3, 6, 13, 9, 5, 7

Table 5.3. State assignments for each algorithm

Table 5.4. KISS2 files for 'non-benchmark' examples

In terms of numbers of literals, the genetic algorithm performed 42% better than NOVA. It should be noted that only the logic for the state variables was considered, as this gives a useful comparison against the partitioning method. Observing the progress of the genetic algorithm it was noted that a state assignment similar to the one produced by NOVA was found after 50 generations on average. NOVA generally performed better than MUSTANG and SPECTRAL. When the output logic was considered the genetic algorithm performed, on average, 33% better than NOVA. On the whole, the partition method and the

genetic algorithm produced similar results if good partitions exist. However, if no partitions exist (as some state tables possess no useful closed partitions), then the genetic algorithm still performs well as its operation is not dependent upon the existence or non-existence of this property. The CPU time consumed by running the GA is longer than that required by other methods, but this is mainly due to the fact that the GA has to communicate with ESPRESSO in order to ascertain the fitness of chromosomes. This had to be done by creating files as ESPRESSO is not embedded in the GA. Even allowing for this overhead, the times taken to run the GA were not excessive. For example, the time taken to run 'donfile' (with 24 states) over 500 generations was about 30 minutes on an HP-720 workstation. Further, with the improving processing speeds of modern workstations, this is likely to become less significant in the future.

At this point it should be noted that the genetic algorithm is not a random search. To support this, tests were made where 1000 different chromosomes were generated at random for 'donfile'. The GA started with 10 chromosomes and ran over 100 generations. The results have shown that on average, over 10 tests, the genetic algorithm produced a 40% better result than the random search. In every test run, the GA found a better assignment than that produced by any random search.

6. Conclusions

A genetic algorithm for finding good assignments for synchronous sequential state machines has been written in C, and described above. Tests as to the effectiveness of this approach to the problem were conducted by comparison of performance against commercially available software when operating upon various widely used benchmarks. A number of smaller examples were also included by way of demonstration.

It may be concluded that the state assignments as found by the genetic algorithm were at least as good, but in most cases better, than those derived by the best available methods. In examples where there is a good structure like 'modulo-12', 'shiftreg', 'test', 'table1-3' and 'swma3', the GA and the partitioning method produced significantly simpler next-state equations

It has been demonstrated that the genetic algorithm is a valid method of finding a good state assignment; it is competitive with commercial software, and is not dependent on any particular feature of the sequential machine. Since the fitness function of the genetic algorithm may be easily modified, certain other aspects of machine design such as: propagation time, the use of flip-flops other than D-type, or the use of one gate type as opposed to another may be taken into account. Further, it is believed that the effectiveness of the genetic algorithm itself may be enhanced, possibly by better chromosome encoding and more effective parent selection, and that as a consequence improved results may be obtained.

7. References

1.Armstrong, DB, "A programmed algorithm for assigning internal codes to sequential

machines", IRE Trans, 1962, EC11, pp 466-472

2.Dolotta, TA, and McCluskey, E, "The coding of internal states of sequential

machines", IEEE Trans, 1964, EC13, pp 549-562

3.Hartmanis, J, "On the state assignment problem for sequential machines I", IRE

Trans, 1961, EC10, pp 157-165

4.Stearns, RE, and Hartmanis, J, "On the state assignment problem for sequential

machines II", IRE Trans, 1961, EC10, pp 593-603

5.Villa, T, and Sangiovanni-Vincentelli, A, "NOVA: State assignment of finite state

machines for optimal two level logic implementation", IEEE Trans, 1990, C-9, pp

905-924

6.Devadas, S, Ma, HT, Newton, AR, and Sangiovanni-Vincentelli, A, "MUSTANG:

State assignment of finite state machines for optimal multi-level logic

implementations", presented at ICCAD, 1987

7.Holland, JM, "Adaptation in Natural and Artificial Systems", Ann Arbor-University

of Michigan Press, 1975

8.Goldberg, DE, "Genetic Algorithms in Search, Optimization and Machine Learning.",

Addison-Wesley, 1989.

9.Hancock, PJ, "An empirical comparison of selection methods in evolutionary

algorithms," AISB Workshop on Evolutionary Computing., at Leeds University,

UK, April 1994.

10.McCluskey, EJ, and Unger, SH, "A note on the number of internal variable

assignments for sequential switching circuits", IRE Trans, 1959, EC8, pp 439-440

11.Almaini, AEA, "Electronic Logic Systems", 3rd ed., Prentice-Hall, 1994

12.Perin, JP, Denouette, M, and Daclin, E, "Switching Machines, Vol 2", D Reidel, 1972

13.Kohavi, Z, "Switching and Finite State Automata Theory.", McGraw-Hill, 1970.

旅游与文化 翻译

旅游与文化I Part I 1．charming autumn scenery in a most fresh air and clear weather 秋高气爽，秋色宜人 2．the 15th General Assembly Session of the World Tourism Organization 世界旅游组织第15届全体大会3．to travel ten thousand li and read ten thousand books 读万卷书，行万里路 4．enriching themselves mentally and physically 承天地之灵气，接山水之精华 5．tourist arrival 旅游人数 6．foreign currency receipts 外汇收入 7．outbound tourists 出境旅游人数 8．unique, rich and varied tourism resources 得天独厚的旅游资源 9．World Cultural and Natural Heritages sites 世界文化遗产地和世界自然遗产地 10．t o add radiance and charm to each other 交相辉映 11．a thriving modern metropolis 繁华的现代化大都市 12．a patchwork of cottages 村舍星罗棋布 13．t o exist side by side 鳞次栉比 14．I nternational Architecture Exhibition 万国建筑博览会 15．c lock towers and turrets , marble pillars 钟塔、角楼和大理石柱 16．e ach representing a distinctively individual appearance 风格迥异，各领风骚 17．t he rainy season 梅雨季节 18．t o linger longer 留连忘返 19．e xcellence, elegance and the best quality 卓越超群，富丽堂皇，一流质量 20．e mbroidery, inlaid lacquer 刺绣，金漆镶嵌 21．g old and silver jewelleries, water-color woodblock prints 金银首饰，木刻水印 22．c arvings in jade, ivory, bamboo and woven bamboo baskets 玉雕、牙雕，竹雕，竹编筐篮 23．b ird cages, lanterns 鸟笼灯笼 24．d ouble-sided embroidery and sandal wood fans from Suzhou 双面绣和苏州的檀香扇 25．t erracotta teapots from Yixing, and clay figures from Wuxi 宜兴的陶制茶壶和无锡的泥人 26．t he Peach Blossom Fair 桃花节 27．t he Daci Temple Fair 大慈寺庙会 28．t he Chengdu Tourism Festival 成都旅游节 29．a place blessed with favorite climate, fertile land, rich resources and outstanding talents 物华天宝，人杰地灵30．s uperb artistic style of aiming at catching the sprit of the landscape 写意山水 31．a rtistic gems 艺术瑰宝 32．U NESCO Heritage Committee 联合国教科文组织遗产委员会 33．t he list of World cultural heritage 世界文化遗产名录 34．b ronzeware 青铜器 35．b amboo, wood and lacquer ware 竹木漆器 36．i nscribed bones and tortoise shells 甲骨 37．s eals 玺印 38．a rchaeology 39．r estoration room 文物修复馆 旅游与文化II Part II景点描述常用语 Match work: 雄伟壮丽imposing 灯火辉煌glittering

国际会计准则第36号

国际会计准则第号资产减值 国际会计准则第号 目的 本准则的目的是，规定企业用以确保其资产以不超过可收回价值（）的金额进行计量的程序。如果资产的帐面价值超过通过使用或销售而收回的价值，该资产就是按超过其可收回价值计量的，如果是这样，该资产应视为已经减值，本准则要求企业确认资产减值损失。本准则也规定了企业何时应冲回资产减值损失，以及减值资产的有关披露内容。 范围 .本准则适用于除下述资产以外的所有资产减值的会计核算： （）存货（参见《国际会计准则第号存货》）； （）建造合同形成的资产（参见《国际会计准则第号建造合同》）； （）递延所得税资产（参见《国际会计准则第号所得税》）； （）雇员福利形成的资产（参见《国际会计准则第号雇员福利》）； （）包括在《国际会计准则第号金融工具：披蹲和列报》范围内的金融资产。 .本准则不适用于存货、建造合同形成的资产、递延所得税资产或雇员福利形成的资产，因为适用于这些资产的现行国际会计准则已经包含了有关其确认和计量的特定要求。 .包括在《国际会计准则第号金融工具：披落和列报》范围内的金融资产，其减值损失的会计核算取决于国际会计准则委员会有关金融工具项目的结果。以下投资属于金融资产，但《国际会计准则第号金融工具：披露和列报》没有包括，因而本准则也适用于这些金融资产： （）《国际会计准则第号合并财务报表和对子公司投资的会计》中定义的子公司； （）《国际会计准则第号对联营企业投资的会计》中定义的联营企业； （）《国际会计准则第号合营中权益的财务报告》中定义的合营企业。 .根据其他国际会计准则，以重估价值（公允价值）计量的资产，如按《国际会计准则第号固定资产》以重估价值作为允许选用的方法计量的资产，本准则也适用。但是，认定某项重估资产是否已经减值，取决于用以确定其公允价值的基础： （）如果资产的公允价值是其市场价值，则资产的公允价值与其销售净价之间的唯一差额是处置该资产的直接增量费用：

《论语十则》——《中国文化经典研读》(整理)

《论语十则》——《中国文化经典研读》（整理） 1 子曰:“君子食无求饱，居无求安，敏于事而慎于言，就(1)有道(2)而正(3)焉，可谓好学也已。” 【注释】 (1)就:靠近、看齐。 (2)有道:指有道德的人。 (3)正:匡正、端正。 【译文】 孔子说:“君子，饮食不求饱足，居住不要求舒适，对工作勤劳敏捷，说话却小心谨慎，到有道的人那里去匡正自己，这样可以说是好学了。” 2 2?4 子曰:“吾十有(1)五而志于学，三十而立(2)，四十而不惑(3)，五十而知天命(4)，六十而耳顺(5)，七十而从心所欲不逾矩(6)。” 【注释】 (1)有:同“又”。 (2)立:站得住的意思。 (3)不惑:掌握了知识，不被外界事物所迷惑。 (4)天命:指不能为人力所支配的事情。 (5)耳顺:对此有多种解释。一般而言，指对那些于己不利的意见也能正确对待。 (6)从心所欲不逾矩:从，遵从的意思;逾，越过;矩，规矩。 【译文】 孔子说:“我十五岁立志于学习;三十岁能够自立;四十岁能不被外界事物所迷惑;五十岁懂得了天命;六十岁能正确对待各种言论，不觉得不顺;七十岁能随心所

欲而不越出规矩。” 3 子曰:“由(1)，诲女(2)，知之乎,知之为知之，不知为不知，是知也。” 【注释】 (1)由:姓仲名由，字子路。生于公元前542年，孔子的学生，长期追随孔子。 (2)女:同汝，你。 【译文】 孔子说:“由，我教给你怎样做的话，你明白了吗,知道的就是知道，不知道就是不知道，这就是智慧啊～” 4.颜渊、季路侍(1)。子曰:“盍(2)各言尔志。”子路曰:“原车马，衣轻裘，与朋友共，敝之而无憾。”颜渊曰:“愿无伐(3)善，无施劳(4)。”子路曰:“愿闻子之志。”子曰:“老者安之，朋友信之，少者怀之(5)。” 【注释】 (1)侍:服侍，站在旁边陪着尊贵者叫侍。 (2)盍:何不。 (3)伐:夸耀。 (4)施劳:施，表白。劳，功劳。 (5)少者怀之:让少者得到关怀。 【译文】 颜渊、子路两人侍立在孔子身边。孔子说:“你们何不各自说说自己的志向,”子路说:“愿意拿出自己的车马、衣服、皮袍，同我的朋友共同使用，用坏了也不抱怨。”颜渊说:“我愿意不夸耀自己的长处，不表白自己的功劳。”子路向孔子说:“愿意听听您的志向。”孔子说:“(我的志向是)让年老的安心，让朋友们信任我，让年轻的子弟们得到关怀。” 5 子曰:“知之者不如好之者，好之者不如乐之者。” 【译文】

国际会计科目对照表(中英)

精心整理 ccount?帐户 Accounting?system?会计系统? American?Accounting?Association?美国会计协会? American?Institute?of?CPAs?美国注册会计师协会? Audit?External?users?外部使用者? Financial?accounting?财务会计? Financial?Accounting?Standards?Board?财务会计准则委员会? Financial?forecast?财务预测? Generally?accepted?accounting?principles?公认会计原则? General-purpose?information?通用目的信息 Government?Accounting?Office?政府会计办公室? ? Management?accounting?管理会计? Return?of?investment?投资回报? Return?on?investment?投资报酬? Securities?and?Exchange?Commission?证券交易委员会?

Statement?of?cash?flow?现金流量表? Statement?of?financial?position?财务状况表? Tax?accounting?税务会计? Accounting?equation?会计等式? Assets? Creditor? Deflation? Disclosure?批露? Expenses?费用? Financial?statement?财务报表? Financial?activities?筹资活动? Going-concern?assumption?持续经营假设Inflation?通货膨涨? Investing?activities?投资活动? Liabilities?负债? Solvency?清偿能力? Stable-dollar?assumption?稳定货币假设? Stockholders?股东? Stockholders?equity?股东权益?

23.国际会计准则

对国际会计准则第8号——当期净损益、重大差错和会计政策变更（1993年修订）的改进建议1 1主译，丁度翻译Invitation to comment, Summary of main changes & Appendix，丁度主校对。

国际会计准则第8号——会计政策、会计估计变更和差错 （200X年修订） （备注：尽管本征求意见稿是以“清样稿”形式列示，上述标题仍以作出标记的形式列示建议的改动。）

征询意见 理事会将特别欢迎对下列问题的回答。意见中最好能指明有关的准则段落，包含明晰的基本原理，并在合适的地方提出备选措辞的建议。 问题1 您是否同意会计政策自愿变更和差错更正的允许选用的处理方法应当被删除，即这些变更和更正应当用追溯调整法处理，如同新的会计政策一直在运用或该差错从未发生那样（参见第20段、21段、32段和33段）？ 问题2 您是否同意删除重大差错和其他重要差错的区别（参见第32段和33段）？ 主要改动摘要 主要的改动建议有： ●通过下列方式修改本准则的范围： ?包含《国际会计准则第1号——会计报表的列报》的第20至22段，该 段详细说明了会计政策选择的标准； ?删除IAS8第7至18段涉及到收益表项目列报的要求。这些要求，包括 所作的修改，将转入IAS1。 因此，本准则的名称该为《会计政策、会计估计变更和差错》。 ●删除重大差错和其他重要差错的区别，并在建议的第3段增加差错的定 义。重大差错的概念被删除 ●删除IAS8第38至40段所列的差错更正允许选用的处理方法。因此，某个 实体不再被允许： ?将差错更正的金额包含在当期损益中；

翻译中国文化和历史

翻译（一）、中国文化和历史 1、狮舞（Lion Dance）是中国最广为流传的民间舞蹈之一。狮为百兽之首，在中国传统中，狮子被视为是能带来好运的吉祥物（mascot）。古人将狮子视作是勇敢和力量的化身，能驱赶邪恶、保护人类。据记载，狮舞已拥有了2,000多年的历史。在唐代（the Tang Dynasty），狮舞就已经被引入了皇室。因此，舞狮成为元宵节（the Lantern Festival）和其他节日的习俗，人们以此来祈祷好运、平安和幸福。 The Lion Dance is one of the most widespread folk dances in China.The lion is the king of animals. In Chinese tradition, the lion is regarded as a mascot, which can bring good luck.Ancient people regarded the lion as a symbol of braveness and strength, which could drive away evil and protect humans. The dance has a recorded history of more than 2,000 years. During the Tang Dynasty, the Lion Dance was already introduced into the royal family of the dynasty. Therefore, performing the lion dance at the Lantern Festival and other festive occasions became a custom where people could pray for good luck, safety and happiness. 2、端午节，又叫龙舟节，是为了纪念爱国诗人屈原。屈原是一位忠诚和受人敬仰的大臣（minister）,他给国家带来了和平和繁荣。但最后因为受到诽谤（vilify）而最终投河自尽。人们撑船到他自尽的地方，抛下粽子，希望鱼儿吃粽子，不要吃屈原的身躯。几千年来，端午节的特色在于吃粽子（glutinous dumplings）和赛龙舟，尤其是在一些河湖密布的南方省份。 ? The Duanwu Festival, also called the Dragon Boat Festival, is to commemorate the patriotic poet Qu Yuan. Qu Yuan was a loyal and highly esteemed minister, who brought peace and prosperity to the state but ended up drowning himself in a river as a result of being vilified. People got to the spot by boat and cast glutinous dumplings into the water, hoping that the fishes ate the dumplings instead of Qu Yuan’s body. For thousands of years, the festival has been marked by glutinous dumplings and dragon boat races, especially in the southern provinces where there are many rivers and lakes. 3、上海菜系是中国最年轻的地方菜系，通常被成为“本帮菜”，有着400多年的历史。同中国其他菜系一样，“本帮菜”具有“色，香，味”三大要素。//上海菜的特点是注重调料的使用，食物的质地和菜的原汁原味。其中最著名的有特色点心“南翔小笼”和特色菜“松鼠鲑鱼”。//“南翔小笼”是猪肉馅，个小味美，皮薄汁醇。“松鼠鲑鱼”色泽黄亮，形如松鼠，外皮脆而内肉嫩，汤汁酸甜适口。//在品尝过“松鼠鲑鱼”之后，我们常常惊讶于“松鼠”的形状，觉得在三大评价标准上在添加“形”这个标准才更合适。 Shanghai cuisine, usually called Benbang cuisine, is the youngest among themajor regional cuisines in China, with a history of more than 400 years. Like all other Chinese regional cuisines, Benbang cuisines takes “color, aroma and taste”as its essential quality elements.//Shanghai cuisine emphasizes in particular the expert use of seasonings,

中英文会计科目对照表

中英文会计科目对照表如下: 会计科目中英对照表会计科目 accounting subject 顺序号serial number 编号code number 会计科目名称accounting subject 会计科目适用范围accounting subject range of application 一、资产类 1 1001 库存现金 cash on hand 2 1002 银行存款 bank deposit 5 1015 其他货币资金 other monetary capital 9 1101 交易性金融资产 transaction monetary assets 11 1121 应收票据 notes receivable 12 1122 应收账款 Account receivable 13 1123 预付账款 account prepaid 14 1131 应收股利 dividend receivable 15 1132 应收利息 accrued interest receivable 21 1231 其他应收款 accounts receivable-others 22 1241 坏账准备 had debts reserve 28 1401 材料采购 procurement of materials 29 1402 在途物资 materials in transit 30 1403 原材料 raw materials 32 1406 库存商品 commodity stocks 33 1407 发出商品 goods in transit 36 1412 包装物及低值易耗品 wrappage and low value and easily wornout articles 42 1461 存货跌价准备 reserve against stock price declining 43 1501 待摊费用 fees to be apportioned 45 1521 持有至到期投资 hold investment due 46 1522 持有至到期投资减值准备 hold investment due reduction reserve

国际会计准则第无形资产

国际会计准则第38号无形资产 1998-06发布 发布时间：2000年08月27日 目的 本准则的目的是对其他国际会计准则中没有具体涉及的无形资产的会计处理进行规范。本准则要求，当且仅当特定条件满足时，企业应确认无形资产。本准则亦对如何计量无形资产的账面金额作了规定，并就无形资产的特定放过提出了要求。 范围 1．本准则应适用于所有企业除以下各项之外的无形资产的会计核算： (1)由其他国际会计准则规范的无形资产； (2)《国际会计准则第32号金融工具：披露和列报》中定义的金融资产； （3）矿产权，以及矿产、石油、天然气和类似非再生性资源的勘探支出或开发和采掘支出；

（4）保险公司与保单持有人之间签订的合同所产生的无形资产。 2．如果其他国际会计准则涉及了特定类型的无形资产，那么企业应运用该项准则而不是本准则。例如，本准则不适用于以下各项无形资产： （1）企业在正常经营过程中为出售而持有的无形资产（见《国际会计准则第2号存货》和《国际会计准则第11号建造合同》）； （2）递延所得税资产（见《国际会计准则第12号所得税》）； （3）属于《国际会计准则第17号租赁》范围内的租赁； （4）雇员福利所形成的资产（见《国际会计准则第19号雇员福利》）； （5）企业合并中形成的商誉（见《国际会计准则第22号企业合并》）； （6）《国际会计准则第32号金融工具：披露和列报》中定义的金融资产。金融资产的确认和计量由以下准则规范：《国际会计准则第27号合并财务报表和对于公司投资的会计》、《国际会计准则第28号在联营企业投资的会计》、《国际会计准则第31号合营中权益的

财务报告》和《国际会计准则第39号金融工具：确认和计量》。 3．一些无形资产可能会以实物为载体，例如磁盘（对计算机软件而言）、法律文件（对许可证或专利权而言）或胶片式确定一项包含无形和有形要素的资产应按《国际会计准则第历号一固定资产》核算，还是作为一项无形资产而按本准则核算，需要进行判断，以评价哪个要素更重要。例如，一台计算机控制的机械工具没有特定计算机软件就不能正常运行时，则说明该软件构成相关硬件不可缺少的组成部分，从而该软件应作为固定资产核算。同样的原则适用于计算机控作系统。如计算机软件不是相关硬件不可缺少的组成部分，则该软件应作为无形资产核算。 4．本准则还适用于广告、培训、开办活动、研究与开发活动支出等。研究与开发活动的目标是开发知识。因此，虽然这些活动可能会产生有实物形态的资产（例如，样品），但该资产的实物要素次于其无形要素（即含在实物要素中的知识）。 5．就融资租赁而言，标的资产可能是有形的，也可能是无形的。初始确认后，承租人应按本准则的规定核算因融资租赁而持有的无形资产。电影、录相、戏剧、手稿、专利权和版权等项目的许可证协议中的权利不在《国际会计准则第对号一租赁》范围之内，而在本准则范围之内。 6．如果某些活动或交易特殊，可能需要用其他方式进行会计处理，那么该活动或交易可能会被排除出某项国际会计准则的范围。采掘业中因石油、天然气和矿产的勘探或开发和采

中级翻译之一(文化与翻译)

Cast P earls before swine.牛一猪） 7）落汤鸡/落水狗：Like a drow ned mouse .鸡/狗一鼠） 8）热锅上的蚂蚁： Like a cat o n hot bricks.蚂蚁一猫） 9）宁为鸡头，毋为牛后： Better be the head of a dog than the tail of a lio n.鸡 —狗，牛一狮） 由此可见，中国成语中的动物在英语成语中已不属于同一动物了。 2. 与颜色有关的成语或谚语英汉也不尽相同 在颜色的作用上，有相同点。比如，红色（red ），无论是在英语国家，还是在 中国，往往都与庆祝活动和喜庆日子有关。如中国有 开门红”代表着好运气。 英语有“redletter days 纪念日，喜庆日）等。但更多的还是有不同点，而且两种 语言在颜色 的意义上也有区别。如 黄色”（yellow ）在西方黄色表示懦弱，胆小， 不敢面对现实。而在中国，黄色是帝王的象征。只有皇帝才配着黄色。再如 蓝 色” （blue ）在英语中通常表示不快活，心情不好。如： 1） He is blue today 文化与翻译 英汉成语、谚语中的语言文化差异 其语言也必然会有很大的差别。语言 来谈 由于中西方的社会文化存在着很大的差异， 离不开文化，文化也离不开语言。本文就英汉成语或谚语对译的几个实例, 谈两种语言在成语或谚语中所存在的文化差异。 1.与动物有关的成语或谚语英汉不尽相同 1）他壮得像头牛。 He is as stro ng as a horse 牛一马） 2）她胆小如 鼠。 She is as timid as a hare （鼠一兔） 3）养虎遗 患: Warm a sn ake in on e's bosom 虎—蛇） 4）瓮中之鳖: Like a rat in a hole.（鳖一鼠） 5）害群之 马: Black shee p.马一羊） 6）对牛弹琴:

会计科目中英对照表

资产Assets 流动资产Current assets 货币资金Cash at bank and on hand 交易性金融资产Financial assets held for trading 应收票据Notes receivable 应收账款Accounts receivable 预付款项Advances to suppliers 应收利息Interest receivable 应收股利Dividends receivable 其他应收款Other receivables 存货Inventories 一年内到期的非流动Current portion of non-current assets

资产 其他流动资产Other current assets 流动资产合计Total current assets 非流动资产Non-current assets 可供出售金融资产Available-for-sale financial assets 持有至到期投资Held-to-maturity investments 长期应收款Long-term receivables 长期股权投资Long-term equity investments 投资性房地产Investment properties 固定资产Fixed assets 在建工程Construction in progress 工程物资Construction materials

固定资产清理Fixed assets pending for disposal 生产性生物资产Bearer biological assets 油气资产Oil and gas assets 无形资产Intangible assets 开发支出Development costs 商誉Goodwill 长期待摊费用Long-term prepaid expenses 递延所得税资产Deferred tax assets 其他非流动资产Other non-current assets 非流动资产合计Total non-current assets 资产总计Total assets

企业会计准则第38号——首次执行企业会计准则

企业会计准则第38号——首次执行企业会计准则 第一章总则 第一条为了规范首次执行企业会计准则时的确认、计量和财务报表列报，根据《企业会计准则——基本准则》，制定本准则。 第二条首次执行企业会计准则，是指企业第一次执行2006年发布的企业会计准则体系，包括基本准则、各项具体准则和会计准则应用指南。 第三条首次执行企业会计准则后发生的会计政策变更，适用《企业会计准则第28号——会计政策、会计估计变更和差错更正》。 第二章确认和计量 第四条在首次执行日，企业应当对所有资产、负债和所有者权益按照企业会计准则的规定进行重新分类、确认和计量，并编制期初资产负债表，本准则规定不应追溯调整的除外。 第五条对于首次执行日的长期股权投资，应当分别下列情况处理： (一)对于按照《企业会计准则第20号——企业合并》规定，属于同一控制下企业合并产生的长期股权投资，尚未摊销完毕的股权投资差额全额冲销，并调整留存收益，以冲销股权投资差额后的长期股权投资账面价值作为首次执行日的认定成本。 (二)除上述第(一)项以外的其他采用权益法核算的长期股权投资，存在股权投资贷方差额的，应冲销贷方差额，调整留存收益，并以冲销贷方差额后的长期股权投资账面价值作为首次执行日的认定成本；存在股权投资借方差额的，应当将长期股权投资的账面价值作为首次执行目的认定成本。 第六条在首次执行日，企业应当根据《企业会计准则第3号——投资性房地产》，将符合投资性房地产定义、原包括在固定资产(或无形资产、存货)账面价值中的相应金额，确认为投资性房地产。 对于有确凿证据表明可以采用公允价值模式计量的投资性房地产，应当按照其公允价值进行计量，账面价值与公允价值的差额，调整留存收益。 第七条在首次执行日，对于符合预计负债确认条件且该日之前尚未计入固定资产成本的弃置费，应当增加该项资产成本和负债；同时，将应补提的折旧调整留存收益。

国际会计科目中英文对照

国际会计科目中英文对照Account帐户 Accounting system会计系统AmericanAccountingAssociation美国会计 协会 AmericanInstituteofCPAs美国注册会计师 协会 Audit审计 Balance sheet资产负债表 Bookkeepking簿记 Cash flow prospects现金流量预测 Certificate in Internal Auditing内部审计证书CertificateinManagementAccounting管理 会计证书 Certificate Public Accountant注册会计师 Cost accounting成本会计 External users外部使用者 Financial accounting财务会计 Financial Accounting Standards Board财务会 计准则委员会 Financial forecast财务预测

Generallyacceptedaccountingprinciples公认会计原则 General-purpose information通用目的信息GovernmentAccountingOffice政府会计办公室 Income statement损益表InstituteofInternalAuditors内部审计师协会InstituteofManagementAccountants管理会计师协会 Integrity整合性 Internal auditing内部审计 Internal control structure内部控制结构Internal Revenue Service国内收入署Internal users内部使用者 Management accounting管理会计Return of investment投资回报 Return on investment投资报酬SecuritiesandExchangeCommission证券交易委员会 Statement of cash flow现金流量表

企业会计准则第38号

企业会计准则第38号 财会[2006]3号 第一章总则 第一条为了规范首次执行企业会计准则对会计要素的确认、计量和财务报表列报，依照《企业会计准则——差不多准则》，制定本准则。 第二条首次执行企业会计准则，是指企业第一次执行企业会计准则体系，包括差不多准则、具体准则和会计准则应用指南。 第三条首次执行企业会计准则后发生的会计政策变更，适用《企业会计准则第28号——会计政策、会计估量变更和差错更正》。 第二章确认和计量 第四条在首次执行日，企业应当对所有资产、负债和所有者权益按照企业会计准则的规定进行重新分类、确认和计量，并编制期初资产负债表。 编制期初资产负债表时，除按照本准则第五条至第十九条规定要求追溯调整的项目外，其他项目不应追溯调整。 第五条关于首次执行日的长期股权投资，应当分别下列情形处理： （一）依照《企业会计准则第20号——企业合并》属于同一操纵下企业合并产生的长期股权投资，尚未摊销完毕的股权投资差额应全额冲销，并调整留存收益，以冲销股权投资差额后的长期股权投资账面余额作为首次执行日的认定成本。 （二）除上述（一）以外的其他采纳权益法核算的长期股权投资，存在股权投资贷方差额的，应冲销贷方差额，调整留存收益，并以冲销贷方差额后的长期股权投资账面余额作为首次执行日的认定成本； 存在股权投资借方差额的，应当将长期股权投资的账面余额作为首次执行日的认定成本。 第六条关于有确凿证据说明能够采纳公允价值模式计量的投资性房地产，在首次执行日能够按照公允价值进行计量，并将账面价值与公允价值的差额调整留存收益。 第七条在首次执行日，关于满足估量负债确认条件且该日之前尚未计入资产成本的弃置费用，应当增加该项资产成本，并确认相应的负债；同时，将应补提的折旧（折耗）调整留存收益。 第八条关于首次执行日存在的解除与职工的劳动关系打算，满足《企业会计准则第9号——职工薪酬》估量负债确认条件的，应当确认因解除与职工的劳动关系给予补偿而产生的负债，并调整留存收益。

《最新国际会计科目中英文对照》(2013.9)

中国会计科目中英文对照 代码名称代码名称代码名称代码名称英译 1 资产assets 11~ 12 流动资产current assets 111 现金及约当现金cash and cash equivalents 1111 库存现金cash on hand 1112 零用金/周转金petty cash/revolving funds 1113 银行存款cash in banks 1116 在途现金cash in transit 1117 约当现金cash equivalents 1118 其它现金及约当现金other cash and cash equivalents 112 短期投资short-term investment 1121 短期投资-股票short-term investments - stock 1122 短期投资-短期票券short-term investments - short-term notes and bills 1123 短期投资-政府债券short-term investments - government bonds 1124 短期投资-受益凭证short-term investments - beneficiary certificates 1125 短期投资-公司债short-term investments - corporate bonds 1128 短期投资-其它short-term investments - other 1129 备抵短期投资跌价损失allowance for reduction of short-term investment to market 113 应收票据notes receivable 1131 应收票据notes receivable 1132 应收票据贴现discounted notes receivable 1137 应收票据-关系人notes receivable - related parties 1138 其它应收票据other notes receivable 1139 备抵呆帐－应收票据allowance for uncollec- tible accounts- notes receivable 114 应收帐款accounts receivable 1141 应收帐款accounts receivable 1142 应收分期帐款installment accounts receivable 1147 应收帐款-关系人accounts receivable - related parties 1149 备抵呆帐－应收帐款allowance for uncollec- tible accounts - accounts receivable 118 其它应收款other receivables 1181 应收出售远汇款forward exchange contract receivable 1182 应收远汇款-外币forward exchange contract receivable - foreign currencies 1183 买卖远汇折价discount on forward ex-change contract 1184 应收收益earned revenue receivable 1185 应收退税款income tax refund receivable 1187 其它应收款- 关系人other receivables - related parties 1188 其它应收款- 其它other receivables - other 1189 备抵呆帐- 其它应收款allowance for uncollec- tible accounts - other receivables 121~122 存货inventories 1211 商品存货merchandise inventory 1212 寄销商品consigned goods

新会计准则会计科目表(中英文对照)知识分享

一、资产类 1 1001 库存现金cash on hand 2 1002 银行存款bank deposit 5 1015 其他货币资金other monetary capital 9 1101 交易性金融资产transaction monetary assets 11 1121 应收票据notes receivable 12 1122 应收账款Account receivable 13 1123 预付账款account prepaid 14 1131 应收股利dividend receivable 15 1132 应收利息accrued interest receivable

21 1231 其他应收款accounts receivable-others 22 1241 坏账准备had debts reserve 28 1401 材料采购procurement of materials 29 1402 在途物资materials in transit 30 1403 原材料raw materials 32 1406 库存商品commodity stocks 33 1407 发出商品goods in transit 36 1412 包装物及低值易耗品wrappage and low value and easily wornout articles 42 1461 存货跌价准备reserve against

stock price declining 45 1521 持有至到期投资hold investment due 46 1522 持有至到期投资减值准备hold investment due reduction reserve 47 1523 可供出售金融资产financial assets available for sale 48 1524 长期股权投资long-term stock ownership investment 49 1525 长期股权投资减值准备long-term stock ownership investment reduction reserve 50 1526 投资性房地产investment real eastate 51 1531 长期应收款long-term account

文化经典选修教材重点翻译(附带重点字词和参考译文)

要求： 1.通读全句，划出采分点（实词、虚词、句式），然后翻译。 2.翻译后，通读自己的译文，检查有无舛漏。 3.这里只是选取了课文里的部分重点句子以帮助大家复习，会考小本里的课外文言文段落还需大家用心温习。 《晋灵公不君》 1.将谏，士季曰：“谏而不入，则莫之继也。会请先，不入，则子继之。” （赵盾）准备进谏，士季说：?（您是正卿，您）去进谏，如果国君不接受，那就没有谁能接着进谏了。我请求先去进谏,假如国君不采纳,那么您再继续进谏。? 2.犹不改。宣子骤谏，公患之，使鉏麑贼之。 (晋灵公)仍旧不改。赵宣子多次进谏。晋灵公很厌恶他，（就）派鉏麑（去）刺杀他。 3.晨往，寝门辟矣，盛服将朝。尚早，坐而假寐。 （鉏麑）清早赶去，看到卧室的门已打开了。(赵宣子)已穿戴整齐准备上朝，由于时间还早，端坐在那里打瞌睡。 4.公嗾夫獒焉。明搏而杀之。盾曰：“弃人用大，虽猛何为！”斗且出。提弥明死之。 晋灵公唤出那条猛犬向赵盾扑去。提弥明徒手搏击猛犬,把它打死了。赵盾说：?不用人而使唤狗,即使凶猛,又顶得了什么??一面搏斗,一面退出宫门.提弥明为赵盾战死（殉难）。 5.初，宣子田于首山，舍于翳桑。 当初，赵宣子在首阳山打猎，在翳桑住了一晚。 6.使尽之，而为之箪食与肉，置诸橐以与之。既而与为公介，倒戟以御公徒，而免之。 （赵盾）要他吃光,并给他预备一筐饭和肉,放在袋子里送给他。不久灵辄做了晋灵公的甲士,却把戟掉过头来抵御灵公手下的人,使赵盾得免于难。 7.对曰：“子为正卿，亡不越竟，反不讨贼，非子而谁？” （太史）回答说:?您是正卿,逃亡没有越过国境,回来后又不声讨叛贼,弑君的不是您又是谁?? 《求谏》 1.朕每闲居静坐，则自内省，恒恐上不称天心，下为百姓所怨。但思正人匡谏，欲令耳目外通，下无怨滞。又比见人来奏事者，多有怖惧，言语致失次第。寻常奏事，情犹如此，况欲谏诤，必当畏犯逆鳞。 我每当无事静坐,就自我反省。常常害怕对上不能使上天称心如意,对下被百姓怨恨。只想得到正直忠诚的人匡救劝谏,好让我的视听能和外边相通,使下面没有积怨。此外近来见到来奏事的人,常显得心怀恐惧,连讲话也变得语无伦次.平时奏事,情况尚且如此,何况要折面谏诤,必然害怕触犯逆鳞. 2.臣闻木从绳则正，后从谏则圣。是故古者圣主必有争臣七人，言而不用，则相继以死。陛下开圣虑，纳刍荛，愚臣处不讳之朝，实愿罄其狂瞽。

国际会计科目对照表中英

ccount?帐户 Accounting?system?会计系统? American?Accounting?Association?美国会计协会? American?Institute?of?CPAs?美国注册会计师协会? Audit?审计? Balance?sheet?资产负债表? Bookkeepking?簿记? Cash?flow?prospects?现金流量预测? Certificate?in?Internal?Auditing?内部审计证书? Certificate?in?Management?Accounting?管理会计证书? Certificate?Public?Accountant注册会计师? Cost?accounting?成本会计? External?users?外部使用者? Financial?accounting?财务会计? Financial?Accounting?Standards?Board?财务会计准则委员会? Financial?forecast?财务预测? Generally?accepted?accounting?princip les?公认会计原则? General-purpose?information?通用目的信息 Government?Accounting?Office?政府会计办公室? Income?statement?损益表? Institute?of?Internal?Auditors?内部审计师协会? Institute?of?Management?Accountants?管理会计师协会? Integrity?整合性? Internal?auditing?内部审计?