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Industrial and Labor Relations Review, Vol. 54, No. 4 (July 2001). ? by Cornell University.
0019-7939/00/5404 $01.00
A
AN EXPERIMENTAL STUDY OF JOB EVALUATION AND COMPARABLE WORTH
E. JANE ARNAULT, LOUIS GORDON,
DOUGLAS H. JOINES, and G. MICHAEL PHILLIPS*
The doctrine of comparable worth rests on an assumption that each job possesses an inherent worth independent of the market forces of supply and demand. Implementation of comparable worth further requires that inherent job worth be measured with reasonable accuracy.This paper reports the results of an experimental study of comparable worth. Three commercial job evaluation firms rated the same set of 27jobs in an actual company. Statistical analysis of the experimental data indicates that the three evaluators differed in which job trait, or constel-lation of traits, they used to evaluate inherent job worth, implying that at least one of them failed to measure inherent job worth accurately.These results suggest that any attempt to implement comparable worth may be quite sensitive to the evaluator chosen to measure job worth.
*E. Jane Arnault is President of JurEcon, Inc.,Louis Gordon is a statistician at Tradar, LLC., Dou-glas H. Joines is Professor of F inance and Business Economics in the Marshall School of Business, Uni-versity of Southern California, and G. Michael Phillips is Professor of Finance, Real Estate, and Insurance at California State University, Northridge. The authors thank Delores Conway, John Rolph, Peter Rupert,and Mark Zupan for helpful comments on earlier drafts.
An appendix containing more detail on the statis-tical properties of the tests reported in this paper is available from Douglas H. Joines, Department of Finance and Business Economics, Marshall School of Business, University of Southern California, Los An-geles, CA 90089–1427.
dvocates of comparable worth contend that rates of compensation established in unregulated labor markets are frequently inequitable. Economists have long recog-nized that free labor markets may result in a highly unequal distribution of income,and inequality of income has been a source of concern to many observers of labor mar-kets. Those seeking to modify the income distribution set by the market fall into two
basic groups. One group recognizes that different individuals may be endowed with greatly different talents. Even if these tal-ents are appropriately priced by the mar-ket, the resulting income distribution may be very unequal. This group generally ad-vocates income redistribution through gov-ernment tax and transfer programs, but does not necessarily call for direct regula-tion of the labor market.
A second group has much less faith in the workings of the market. They believe that labor markets set inappropriate rates
JOB EVALUATION AND COMPARABLE WORTH807
of pay and need to be regulated. One type of regulation justified by such views is mini-mum wage legislation, which is proposed as a method for raising the earnings of low-income workers.
Another frequently proposed form of labor market regulation is the implementa-tion of a comparable worth plan. The inequities that comparable worth addresses need not be due to a low absolute wage for a given worker, however. These inequities result from a wage considered too low rela-tive to that paid another job, even if both of the jobs in question are highly paid relative to the average job. The labor market regu-lation implied by comparable worth is cor-respondingly more complex than impos-ing a simple minimum rate of pay. It in-volves “fine-tuning” an employer’s entire compensation structure from top to bot-tom.
The information required for this fine-tuning is considerable. It is not sufficient simply to rank jobs according to their sup-posed inherent worth. One must also mea-sure the value of each job and calculate degrees of difference between the values of different jobs. These values are presum-ably not determined merely by someone’s subjective notion of the moral worth of an activity. They are alleged to be something concrete, objective, and measurable.
In this paper we study the measurements of job value that are required for the suc-cessful implementation of comparable worth plans. In particular, we ask whether independent assessments of job worth by several different commercial job evalua-tion firms are mutually consistent. Sub-stantial inconsistencies in these job ratings would cast serious doubt either on the use-fulness of comparable worth as a concept or on the possibilities for applying this concept in practice.
This paper reports the results of an ex-periment in which three commercial job evaluation firms rated the same 27 jobs in a single company. All three firms generate a substantial amount of annual revenue from job evaluation, and each has major corpo-rate clients. The paper presents a statistical test of the hypothesis that all three job evaluation methods measure a single, com-mon trait that might be interpreted as job worth. As noted below, many economists might question whether one can meaning-fully speak of the inherent value of a job independent of market conditions. F or purposes of this study, we do not take a position on this issue. Rather, in imple-menting our test we implicitly assume the existence of a unique trait that might or might not correspond to inherent job worth. We then ask whether the three job evalua-tors have all measured this trait.
The Comparable Worth Movement Comparable worth is one of several poli-cies proposed to reduce the gap between men’s and women’s earnings. The exist-ence of this gap has been widely docu-mented (see, for example, Aaron and Lougy 1986; Johnson and Solon 1986; Blau 1998). There are several ways of accounting for (as opposed to explaining) this pay gap. One way is to identify where the differences in pay arise. They may be accounted for by (i) systematic differences in the pay of men and women performing the same job for the same employer, (ii) a tendency, within a given organization, for women to be con-centrated in lower-paying jobs, and (iii) a tendency for women to work for lower-paying employers. Empirically, item (i) contributes little to the overall gap between men’s and women’s earnings.1 Items (ii) and (iii) are much more important. Aaron and Lougy (1986) reported extensive gen-der segregation of the work force by occu-pation and industry. They and other re-searchers have documented a negative re-lation between industry or occupational earnings and the proportion of workers in the industry or occupation who are female (Bielby and Baron 1986; Johnson and So-lon 1986; Sorensen 1986).2
1See, for example, Ferber and Spaeth (1984), who cited evidence reported by Whitman (1973).
2Blau (1998) documented a reduction of occupa-tional sex segregation. Part of the narrowing of the male-female earnings gap that she found is due to “occupational upgrading” by women.
808INDUSTRIAL AND LABOR RELATIONS REVIEW
The notion that there are “women’s jobs”and “men’s jobs” (for example, nurses and construction workers) and that women’s jobs pay systematically less than men’s pro-vides the rationale for the comparable worth movement. Comparable worth would man-date equal pay within an organization for jobs deemed to be of comparable value. There is some dispute about how much of the difference in earnings between men and women is accounted for by gender segregation within organizations (item ii above) and how much is due to segregation across employers (item iii). This distinc-tion is crucial in judging how far compa-rable worth would go in closing the pay gap between men and women.
Johnson and Solon (1986) estimated that comparable worth would reduce the earn-ings gap only from the current 33.7% of male earnings to between 31.5% and 32.8%. Using data from comparable worth studies of four state governments, Sorensen (1986) concluded that comparable worth would eliminate almost half of the pay gap not already explained by job attributes. Using data for the entire economy, Sorensen (1990) found that within-firm differences in the gender composition of jobs explain 20–23% of the national sex-based earnings gap, as compared with the 14% implied by the analysis of Johnson and Solon. This is the portion of the earnings difference that could be reduced or eliminated if compa-rable worth were implemented throughout the economy.
Data on employment and earnings are sufficient to identify gender segregation of the work force as the primary mechanism generating the earnings gap between men and women. The data are much less clear on the reasons for this gender segregation or for the pay gap more generally. Part may arise because of discrimination in hiring or compensation practices by employers.3 Part may be due to characteristics of individual workers, such as education or experience, that affect their productivity in and qualifi-cation for a given job. Any systematic dif-ferences between men and women in these relevant characteristics would be reflected in a difference between average male and female earnings and in some degree of gender segregation in employment. F i-nally, gender differences in occupational choice may reflect not only employer dis-crimination and gender-related differences in productivity in particular jobs, but also any systematic differences between men and women in their preferences for differ-ent types of work.4
Macpherson and Hirsch (1995) analyzed why female-dominated jobs pay less than male-dominated jobs. They used longitu-dinal data so as to control for unobserved differences in productivity, tastes, and job characteristics better than earlier studies were able to do. They found a negative simple correlation across occupations be-tween wages and the percentage of job incumbents who were female. Use of longi-tudinal data lowered the effect of the pro-portion female by about one-half, “indicat-ing that [unobserved] person-specific la-bor quality or preferences account for much of the previously observed relationship.”They found that adding variables to control for occupational and industry characteris-tics further weakened the effect, and that the “remaining effects of gender composi-tion on female or male wages appear rather small.”5
3Such discrimination must be based on the prefer-ences of employers rather than on an attempt to
minimize labor cost. An employer with monopsony power in the labor market would pay lower salaries to
workers with smaller labor supply elasticities. Labor market studies generally show that women have higher labor supply elasticities than men. Madden (1973) has argued that women may nevertheless have lower labor supply elasticities than men to certain occupa-tions, leading a monopsonistic employer to pay women less than men in those occupations. Aaron and Lougy (1986:6) concluded that at least half of the earnings gap “can be explained by factors unrelated to any possible discrimination by individual employers.”
4See Killingsworth (1987) for a detailed analysis of the effects of comparable worth in a general equilib-rium model in which men and women have different tastes for various jobs.
5In addition to research aimed at accounting for the male-female earnings gap, a body of work has appeared that analyzes the effects of comparable
JOB EVALUATION AND COMPARABLE WORTH809
Unlike the studies that have attempted to account for the male-female earnings gap or to determine the effects of compa-rable worth on particular classes of work-ers, this paper deals with the job evaluation process itself. Because the use of job evalu-ation extends beyond comparable worth systems, this evidence is potentially of in-terest outside the comparable worth con-text.
Job Evaluation
The notion of comparable worth is based on the belief that each job possesses some inherent and quantifiable worth indepen-dent of the value placed on it by the market. Implementation of comparable worth re-quires some way of measuring job value. Job evaluation, which has become wide-spread in the United States since World War II, is proposed as a way of doing this. There are now national and regional firms that provide job evaluation services to em-ployers. While there is no single, standard-ized procedure for job evaluation, some general principles do exist.
One such principle is that job worth depends on the characteristics of the job, not those of the workers who hold the job (see Schwab 1980, quoting Dunn and Rachel 1971, and Bellak 1984). As Schwab (1980) pointed out, this notion of worth is an internal one. The value of a job can be determined only within the context of a particular organization, and a given job can presumably have different values to different organizations.6
Despite differences in detail, various job evaluation procedures involve some com-mon steps. First, job descriptions are writ-ten for the jobs in question. Then a set of relevant job characteristics, called compens-able f actors, is identified and a weight is assigned to each factor. These factors fre-quently include the degree of skill, educa-tion, and mental effort required to per-form the job, the pleasantness of the work-ing conditions, and the responsibility in-volved. F inally, the job descriptions are evaluated to determine how much of each compensable factor each job entails. Schwab (1980) identified several prob-lems with the idealized view that job evalu-ation measures the value of all jobs in an organization. He argued that there is no evidence of the construct validity of job evalu-ation, that is, no evidence that the resulting job scores are related to the construct (job worth) they are supposed to measure. He pointed out that construct validation re-quires that the construct be clearly defined, and (p. 59) that “job worth has not been adequately defined nor has a consensus emerged as to its meaning.”7 Schwab also argued that the idealized view does not correspond to job evaluation in practice. He asserted that firms use job evaluation to set rates of pay for jobs for which the exter-nal market is too thin to provide readily observable indicators of value. In effect, the practical application of job evaluation involves fitting a hedonic price equation to those jobs within an organization for which there are readily identifiable market val-ues. The hedonic price model is then used to set the compensation of jobs for which market values are difficult to observe. This view is similar to that of Aaron and Lougy (1986:27), who stated that job evaluation has been used to remove anomalies from wage structures otherwise found to be ac-ceptable, not to change the wage structures in fundamental ways. The objective has been to align wages for particular jobs with
worth on different classes of workers (see, for ex-ample, Smith 1988; Orazem and Mattila 1990; Orazem, Mattila, and Weikum 1992; and Ames 1995).
6This notion itself raises an interesting issue of equity. Application of a single job evaluation proce-dure to two different companies might lead the com-panies to compensate the same job differently. Cur-rent law requires equal pay for equal work within a single company, and a competitive labor market also tends to result in equal pay for equal work across employers. However, an attempt to enforce equal pay for “comparable” work within each company might
lead different companies to different rates of pay for the same job, resulting in unequal pay for equal work across employers.
7This contrasts with the economist’s notion of marginal productivity, which, quite apart from ques-tions of measurement, has a very precise meaning.
810INDUSTRIAL AND LABOR RELATIONS REVIEW
wages paid for other jobs with similar char-acteristics.
This interpretation, if correct, might explain why firms devote resources to job evaluation rather than merely rely on the market to signal job value. For highly spe-cialized jobs, frequent testing of the market might entail substantial search costs on the part of the employer and the employee, and routine use of job evaluation might reduce the frequency with which these search costs are incurred.
Schwab (1980, 1985) also discussed the convergence of different job evaluation pro-cedures, that is, the strength of the relation between the resulting measures of job worth. Clearly, if different procedures do not measure the same trait (for example, job worth), they cannot all be valid mea-sures of that trait. Schwab noted the “pau-city of research on job evaluation” (1985:41) and concluded (1980:62) that “convergence evidence to date suggests that alternative systems do not yield highly similar results, indicating that at least some systems are not construct valid.”8
This paper examines the convergence of the job evaluations performed by three commercial job evaluation firms. A few previous papers have studied convergence.9 However, none seems to have examined the ratings actually produced by different commercial job evaluation firms, each us-ing its own procedures and staff to perform the evaluations.10
The Experiment
F or this study, three commercial job evaluation firms independently rated the same 27 jobs in a single company. The firms used different factor comparison or point methods developed independently of one another. These methods are similar to the procedure used in the well-known State of Washington comparable worth case. Unlike the Washington case, however, the evaluations in our experiment were based solely on job descriptions prepared by the employer. The evaluators judged these job descriptions to be sufficiently clear and detailed to permit reliable evaluation.
The jobs were chosen in part by stratified random sampling based on the salary and gender of job incumbents. In addition to 24 randomly selected jobs, the three jobs with the largest numbers of employees were included in the sample. Each of these had 50% more incumbents than the next larg-est position in the firm. Together, these three jobs accounted for 22% of the total employment and 19% of the total payroll of the firm. We ranked the 27 jobs in descend-ing order by current salary.
Each evaluator assigned each job a point total referred to as the “size” of the job. The three evaluation procedures used dif-ferent job attributes in computing job sizes. Because the ratings supplied by the evalua-tors are not in dollar units, they provide no direct information about appropriate com-pensation. Because they are not even in the same units, the ratings cannot be compared directly one to another. To facilitate com-parison, we used a linear transformation to
8Another potential difficulty arises in the context of comparable worth because the initial ratings mea-sured by the job evaluation firm are subject to subse-quent modification by other parties to the compa-rable worth process. Orazem and Mattila (1990)
provided a detailed case study of the implementation of a comparable worth plan by the state of Iowa. They found (p. 135) that “initial gains to women were ultimately reduced and redirected toward constitu-encies that stood to lose or gain little as a result of the initial plan: union members, professionals, supervi-sors, and those with the highest market wages.”
9See Madigan (1985) and the studies cited in Schwab (1980, 1985), some of which date back to the 1940s.
10A related issue is the “reliability”—that is, repro-ducibility—of the ratings produced by different evalu-ators using the same evaluation procedure. Although managers at the job evaluation firms participating in this study expressed the belief that experienced staff members would all produce nearly identical ratings, it is possible that some of the differences we note below are attributable to the individual raters rather than to the evaluation procedures. Regardless of whether they result from invalid procedures or unre-liable raters, errors in measuring job worth can have serious consequences. Arvey, Maxwell, and Abraham (1985) showed how measurement errors can lead to apparent gender-related pay differences when no such differences actually exist.
JOB EVALUATION AND COMPARABLE WORTH811 rescale each evaluator’s raw scores so that
they each had a mean of 1,000 and a stan-
dard deviation 250. This normalization was
motivated by the fact that the standard
deviation of the 27 salaries was approxi-
mately 25% of the mean salary.
The Experimental Data
Table 1 shows the transformed scores
assigned to each of the 27 jobs by each of
the three evaluators, as well as the average
salary of all incumbents at the time of the
evaluation, also normalized to have a mean
of 1,000 and a standard deviation of 250.
The left-hand panel of Table 2 is a correla-
tion matrix of the job scores and salary.
The correlation between any two evalua-
tors is high (in the neighborhood of 0.8).
These pairwise correlations are similar to
those reported in the studies surveyed by
Schwab (1980, 1985). It is striking to note
that the third evaluator’s scores have a near-
perfect correlation with salary, whereas the
correlations for the first and second evalu-
ators are noticeably smaller.
The positive association among scores is
not surprising. For example, all evaluators
assigned more points to the job description
entitled “Executive Secretary II” than to
those for “Collator Operator,”“Receiving
Clerk,” and “Office Clerk.” If job evalua-tion is to be useful in implementing compa-rable worth, however, it must measure how much one job is worth relative to another. Here the three evaluators were not in close agreement. F or example, one evaluator assigned 75% more points to the executive secretary than to the collator operator, while another assigned only 30% more points. Similarity among total job scores could occur if each of the evaluators measured and assigned weight to some of the same factors. Similarity between the job scores and salary could occur if some of these factors were also compensated by the mar-ket. The bottom row of Table 2 suggests that this is in fact the case, since all three sets of scores are positively related to salary. This similarity says little about whether the scores are useful in providing measures of inherent job worth that are more “appro-priate” than the market wage. The notion of comparable worth is based on the premise that the market wage does not appropri-ately measure the value of a job. Measures of inherent worth thus constitute suggested corrections to the market wage. If compa-rable worth is to be meaningful in practice, there should be some similarity across evalu-ators not merely in gross measures of job worth, which may simply reflect factors al-ready compensated by the market, but also in the implied adjustments to the market wage.
The right-hand panel of Table 2 shows partial correlations between point scores after the influence of salary is removed. These partial correlations, which might be interpreted as correlations among the sug-gested modifications to existing salary, are considerably lower than the simple correla-Table 1. Normalized Salary and Job Scores.
Evaluator
Job Salary123 11,5401,3751,6901,638 21,4281,2659181,290 31,4181,2891,3481,382 41,3189621,1371,262 51,2401,1658991,182 61,2001,2891,2731,346 71,1441,6831,4191,206 81,1278361,0131,046 91,1169838991,050 101,0891,1471,107986 111,0431,2891,3481,170 121,0329629941,022 131,0239721,1181,022 141,023********* 15895836820890 168881,0531,167890 17888769899874 188801,039820954 19877836820870 208451,1331,013842 21834895918838 22793830899862 23731704820778 24711734850752 25687699696670 26662728696656 27569677598530
812INDUSTRIAL AND LABOR RELATIONS REVIEW
tions reported in the left-hand panel. The fact that they are all positive indicates that the salary adjustments implicitly suggested by the three evaluators are not totally unre-lated to each other. However, the exist-ence of detectable partial correlations among the gross scores does not mean that the implied salary adjustments are strongly enough related to each other to provide unambiguous instructions for modifying the existing pay scale.
Simple and partial correlations provide some evidence on the convergence of the three sets of job evaluations, and these statistics have been reported in some of the earlier literature on the convergence of job evaluations. These correlations do not pro-vide an explicit answer to the question of whether the three sets of ratings measure the same trait, nor have previous studies of convergence. If they do not measure the same trait, then at least some of the ratings must be measuring something other than inherent job worth. An explicit statistical model is required to resolve this issue. We now provide such a model and report the resulting test.
A Statistical Model of Job Evaluation Suppose the score assigned to job j by evaluator e is given by (1)
Y ej = αe + βe W j + εej j = 1, …, N
e = 1, …, M
where αe and βe are parameters, W j is inher-ent job worth, and εej is a random variable with zero mean and variance equal to σ2e
. In addition, assume that cov(W j , εej ) = 0 for all
evaluators and that cov(εej , εfj ) = 0 for any two evaluators e and f .
If inherent job worth were observable,(1) would be a regression equation. The R 2from this regression could be viewed as an indicator of the reliability of evaluator e in measuring job worth. Since job worth is unobservable, (1) is a latent-variable model with a single unobserved factor W j . Latent-variable models have been used extensively in the literature on the measurement of unobservable traits. For example, a model similar to (1) could describe the situation where a test score Y ej is used to measure some underlying ability W j .
If job worth is viewed as a random vari-able, one can without loss of generality assume that it is scaled to have a zero mean and unit variance. In that case, the ana-logue to the regression R 2 is β2e /(β2e + σ2e ),which we will denote by ρ. This is the notion of reliability used in the educational testing literature (see, for example, J ?reskog 1971).11 Note that
Table 2. Correlations between Evaluator Scores and Salary.
Correlations Partial Correlations Controlling for Salary
Evaluator
Evaluator
1
23
12Evaluator 2.82.61Evaluator 3.79.83.48.71Salary .73
.73
.96
—
—
11
Although our usage follows J ?reskog, the term “reliability ” is also used to denote lack of idiosyncratic errors by individual raters using a given evaluation method. Our concern, however, is with the degree to which the different evaluation methods measure the concept of interest —job worth. This latter character-istic is also referred to as validity. Because none of the job evaluation firms regarded lack of inter-rater reli-ability as a serious issue in applying their job evalua-tion methods, we interpret our results as evidence on the validity of the evaluation methods themselves. In addition, although we frequently refer to “evaluator e,” for example, this expression should be regarded as shorthand for “an evaluator using job evaluation method e.”
JOB EVALUATION AND COMPARABLE WORTH
813
(2)
var(Y ej ) = β2e + σ2e
.If a second evaluator f is available, produc-ing a second set of scores Y fj , then (3)
cov(Y ej , Y fj ) = βe βf .
If the number of evaluators M is three, then
the parameters of the system of equations (1) are exactly identified (Barnett 1969;Theobald and Mallinson 1978). If there are three evaluators (called A, B, and C),then the reliability of evaluator A is given by (4)
ρa
= cov(Y aj ,Y bj )cov(Y aj ,Y cj ) cov(Y bj ,Y cj )var(Y aj )
= β2
a
β2a + σ2a
As noted above, this expression is analo-gous to a regression R 2. Also note that ratings from at least two other evaluators, B and C, are required to measure the reliabil-ity of evaluator A. Similar expressions mea-sure the reliabilities of evaluators B and C.These reliabilities can be estimated using the sample analogues to the population variances and covariances in equation (4).Applying this formula to our three sets of job evaluation scores results in estimated reliabilities of 0.78, 0.86, and 0.80 for evalu-ators 1, 2, and 3, respectively.
These estimated reliabilities are appro-priate only if model (1) is correct. In particular, they are appropriate only if all three evaluators measure the same under-lying trait and their scores differ solely because of idiosyncratic measurement er-ror. With only three evaluators, this hy-pothesis cannot be tested.
Regarding salary as a fourth instrument permits such a test, because system (1) is over-identified when M = 4. Salary may be an imperfect measure of inherent job worth W j . If all three evaluators are measuring the single trait inherent job worth, how-ever, they should agree on the reliability of salary (or any other indicator) as a measure of job worth. This implication constitutes the intuitive basis for our test. With three
evaluators, there are three different ways of
expressing the reliability of salary as a mea-sure of inherent job worth. We test the null hypothesis that all three evaluators are measuring the same trait by testing whether these three expressions for the reliability of salary are significantly different from each other.12
In particular, let Y 0j denote salary. Let ρab denote the reliability of salary measured using the scores of evaluators A and B, that is,(5)
ρab
= cov(Y 0j ,Y aj )cov(Y 0j ,Y bj )
cov(Y aj ,Y bj )var(Y 0j )
Let ρac and ρbc be defined in a similar man-ner. Now consider the difference between any two of these expressions for the reli-ability of salary (for example, ρab and ρac ). If model (1) is correct and all three of the evaluators measure the same trait, then the population variances and covariances of their scores are given by (2) and (3) above,and the difference between the two expres-sions for the reliability of salary is given by
(6)
In other words, if model (1) is correct and
all evaluators measure the same trait, all measures of the reliability of salary are equal in population. This forms our null hypoth-esis.13 Under the alternative that the evalu-
.
.
ρab – ρac =
cov(Y 0j ,Y aj )cov(Y 0j ,Y bj )var(Y 0j )
cov(Y 0j ,Y cj )cov(Y aj ,Y bj ) –cov(Y aj ,Y cj )
=
β0βa
β20 + σ20
β0
βa = 0.
–β0βa
12
The test we use is described more fully in an appendix available from the authors. As noted there,our test is related to instrumental variables tech-niques in econometrics, and salary might be regarded as an instrument for the unobservable inherent worth Wj in equation (1).13
The prediction that all reliability measures for salary are equal if evaluators measure the same trait is unaffected by any errors the evaluators themselves may make in measuring that trait. These errors,represented by ε in equation (1), do not affect the covariances in equation (4), and the variances of the measurement errors do not appear in that equa-
814INDUSTRIAL AND LABOR RELATIONS REVIEW
tion. A greater measurement error variance results in a larger standard error for the difference between two reliability measures, however, thus making it more difficult to detect a failure of evaluators to measure the same trait. By reducing an evaluator ’s reliability in measuring a given trait, larger measure-ment error may impair the implementation of a com-parable worth plan.14
The derivation of this distribution and its mo-ments is contained in an appendix available from the authors on request.15
Sensitivity analyses show that these results are robust with respect to the deletion of any one obser-vation from the sample and to logarithmic and square-root transformations of the data. Furthermore, small-sample simulations indicate that the asymptotic re-sults reported here are conservative, that is, that the data probably reject the null hypothesis even more
ators do not all measure the same trait, at least one of the differences is non-zero.Three estimates of the reliability of sal-ary can be computed. The estimated reliabilities are
ρ?12 = 0.65, ρ?13 = 0.89, ρ?23 = 0.84.
Perhaps because of sampling variation,
these three point estimates are not identi-cal. Under the null hypothesis that all evaluators measure the same trait, how-ever, the point estimates should not differ by too much. The pairwise difference be-tween any two of these reliability estimates has an asymptotic normal distribution.14The ratios of the three differences to their standard errors are
ρ?12 – ρ?13 se 12,13= –3.27,
ρ?12 – ρ?23 se 12,23= –2.65,
ρ?13 – ρ?23 se 13,23
= –0.61.
The marginal significance level for the null
hypothesis that all of these differences are zero is 0.003, thus providing strong evi-dence that the three evaluators did not measure the same trait.15
Conclusion
This paper reports the results of an ex-perimental study of comparable worth.Three commercial job evaluation firms rated the same set of 27 jobs in an actual company. Statistical analysis of the experi-mental data indicates that the three evalu-ators did not measure the same trait. This finding implies that not all of the evalua-tors accurately identified and measured a unique, objective value of each job corre-sponding to its “inherent worth ”—the kind of measure that is required for implemen-tation of a comparable worth plan. There are at least two possible explanations for this apparent failure. F irst, many econo-mists would argue that no meaningful no-tion of job value exists other than marginal productivity, and that there is no obvious relation between marginal productivity and the job evaluation procedures commonly used. Second, Schwab (1980) has con-tended that no precise definition of job worth has emerged from the literature on job evaluation and that, as currently prac-ticed, job evaluation does not really try to measure job worth, but instead serves a much more limited purpose.
Regardless of the reason for them, the pronounced differences among the three sets of job scores have important implica-tions for comparable worth. They indicate that inherent job worth is not a construct that is easy to define objectively or to mea-sure reliably. Thus, the scores provided by different job evaluators do not provide mutually consistent adjustments to existing pay scales. The outcome of any attempt to implement comparable worth will depend heavily on which evaluator is chosen to perform the implementation.
strongly than the large-sample test suggests. Details of these results are available from the authors on request.
J ?reskog (1971) discussed a likelihood-ratio test based on the assumption that W j and the εej are normally distributed. We have not relied on the normality assumption.
JOB EVALUATION AND COMPARABLE WORTH815
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需要系数法确定计算负荷： 建筑电气图中：Pe，Pj，Ij，Kx，cosφ各代表什么 建筑电气图中：Pe设备容量，Pjs计算容量，Ijs计算电流，Kx需要系数，cosφ功率因数Pjs=Kx*Pe. Ijs=Pjs/(1.732*Ue*cosφ) , ue=380v -------------------------------------------- 关于：Pjs=Pe*Kx (单相)Ijs=Pjs/0.22CosΦ = (Pjs * 4.5454)/ CosΦ (三相)Ijs=Pjs/0.38*1.732*CosΦ(1.732为3开根号)=( Pjs*1.5193)/CosΦ 上面式中:Pe----负荷总功率；Kx----需用系数；CosΦ---功率因数。 另外也可以根据我提供的符合计算程序进行计算； --------------------------------------------- 负荷计算的目的是为了合理地选择导线截面，确保电气线路和设备经济、安全地运行。常用计算负荷的方法中有“需要系数”法，该法较简单、 精确度较高，且是实用的工程计算方法，因而得到广泛的应用。 一、电器负荷的计算 确定了各用电设备容量之后，将各用电设备分类，即将感性负荷与纯阻性负荷分类。现在民宅中的感性负荷主要有洗衣机、空调器、电冰箱、电风扇、荧光灯中的电感性镇流器；纯阻性负荷主要有电饭（火）锅、电热水器、电热取暖器、白炽灯、加热器等。要进行分类计算。 有功计算负荷等于同类用电设备的容量总和乘以一个需要系数，即 Ｐｊｓ＝Ｋｘ?∑Ｐｅ 式中Ｐｊｓ——有功计算负荷（ｋＷ） ∑Ｐｅ——同类设备的总容量（ｋＷ） Ｋｘ——设备的需要系数，它表示不同性质的民宅对电器负荷的需要和同时使用的一个系数，与用电设备的工作性质、使用效率、数量等因素有关。附表是推荐值，仅供参考。 二、工作电流的计算 由于各类用电设备的功率因素不完全相同，又存在感性负荷和纯阻性负荷，因此应分开计算。 １．计算电流 Ｉｊｓ＝Ｐｊｓ／Ｕｃｏｓ∮ ------------------------------- 电气负荷计算方法与公式 负荷计算的目的是为了合理地选择导线截面,确保电气线路和设备经济、安全地运行。常用计算负荷的方法中有“需要系数”法,该法较简单、精确度较高,且是实用的工程计算方法,因而得到广泛的应用。 一、电器负荷的计算 确定了各用电设备容量之后,将各用电设备分类,即将感性负荷与纯阻性负荷分类。现在民宅中的感性负荷主要有洗衣机、空调器、电冰箱、电风扇、荧光灯中的电感性镇流器;纯阻性负荷主要有电饭(火)锅、电热水器、电热取暖器、白炽灯、加热器等。要进行分类计算。 有功计算负荷等于同类用电设备的容量总和乘以一个需要系数,即 Pjs=Kx?∑Pe 式中Pjs——有功计算负荷(kW)
牛津英语词典又加新字！「Me Time」「Hangry」考下你识几多个！以为你在求学期间已经看毕整本牛津英语词典就代表把全部英文字都学懂了？那你就大错特错啦！事关牛津英语词典每年都会增添新词，例如是网络上很多人在使用的字眼，像selfie这个字。或是一些被大家自行组合的字。只可说牛津英语词典真的是跟着这个世界的潮流在走动！ 1/ Mansplain 这个字相信是由Man+Explain洐生而成，意思是男人在解释事情上所表现的不屑、傲慢态度，一副高高在上的大男人主义。这种情况很多时是发生在而对女性上。 2/ Me Time 这个相信大家都有在使用吧？Me Time即是个人专属时间，不属于他人，是完全可以做自己放松的事情的时间。 3/ Hangry 不要以为编打错字啊！这个是hungry及angry组成的新字，意即因为肚子饿而脾气暴燥发脾气。 4/ Snowflake 雪花这个字本来就已经存在，现时加添了一个新意思，那就是用来形容一些以为自己享有特权或优待的人，是带有贬义的。 5/ Ransomware 用来形容用来进行网络攻击的勒索软件。 6/ Tomoz 大家都懒得写Tomorrow，而写Tomoz。 7/ Dickish 用来形容讨厌或可鄙的人 下面这些字都是与小孩有关的。 8/ BFN BFN=Big Fat Negative 没有怀孕 9/ BFP BFP=Big Fat Positive 怀孕
10/TTC TTC=Trying to conceive 尝试去怀孕 11/CIO CIO=Cry it out 让婴儿哭够就会睡的训练方法 现时很流行即将成为父母的都会在婴儿出世前去一次旅行，如同结婚时的honeymoon，大家叫做babymoon。
利用需要系数法来确定负荷计算 许振西 （厦门华电开关有限公司，福建厦门361006） 1负荷计算的意义和目的 工厂进行电力设计的基本原始资料是各工艺部门提供的用电设备安装容量，这些用电设备种类多，数量大，工作情况复杂。如果只是简单的把各用电设备的容量加起来作为选择导线和电气设备容量的依据，那么必然会造成投资和资源的浪费。因为，各用电设备通常并不同时运行，使用时也并不是都能达到额定容量，工作制也不一样。若估算过小，将造成导线及电气设备因过流而发热，加速绝缘老化，降低使用寿命，严重时引起火灾事故，影响供电系统的正常可靠运行。 为避免这种情况，设计时用的总负荷是一个假想负荷，即计算负荷。计算负荷也称为需要负荷或最大负荷，是根据已知的用电设备安装容量确定的、用于按发热条件选择导体和电气设备时所使用的一个假想的持续负荷。计算负荷产生的热效应与某一段时间内实际变动负荷产生的最大热效应相等。 求计算负荷的这项工作称为负荷计算。负荷计算的目的是为了合理的选择供电系统的导线、开关电器、变压器等设备，使电气设备和材料既能充分得到利用，又能满足电网的安全运行。同时，也是选择仪表量程，整定继电保护的重要依据。 2负荷计算的方法 目前普遍采用的确定计算负荷的方法有需要系数法和二项式法。需要系数法的优点是简便实用，适用于全厂和车间变电所负荷的计算；二项式法适用于机加工车间，有较大容量设备影响的干线和分支干线的负荷计算。《民用建筑电气设计规范》（JGJ/T 16-2008）将需要系数法作为民用建筑电气负荷计算的主要方法，下面将加以详细介绍。 1、将用电设备按类型分组，同一类型的用电设备归为一组，并算出该组用电设备 的设备容量Pe。 对于长期工作制的用电负荷（如空调机组等），其设备容量就是设备铭牌上所标注的额定功率。 对于断续或短时工作制的用电设备，是将额定功率统一换算到负载持续率为25%时的有功功率。电焊机的设备功率是指将额定功率换算到负载持续率为100%时的有功功率。 对于照明设备：白炽灯的设备容量按灯泡上标注的额定功率取值；带自感式镇流器的荧光灯和高压汞灯等照明装置，由于自感式镇流器的影响，不仅功率因数很低，在计算设备容量时，还应考虑镇流器上的功率消耗。因此，对采用自感式镇流器的荧光灯装置，其设备容量取灯管额定功率的倍，高压汞灯装置的设备容量取灯泡额定功率的倍。 另外，成组用电设备的设备功率不应包括备用设备。 2、根据用电设备组的设备容量Pe，即可算出设备的计算负荷： 计算有功功率P c=K x P e 计算无功功率Q c=P c tanφ 计算视在功率S c= 计算电流I c=S c/U n 式中：K x为需要系数，φ为功率因数角，U n为额定线电压。
硕037班 刘文3110038020 2011/4/20交互式多模型仿真与分析IMM算法与GBP算法的比较，算法实现和运动目标跟踪仿真，IMM算法的特性分析 多源信息融合实验报告
交互式多模型仿真与分析 一、 算法综述 由于混合系统的结构是未知的或者随机突变的，在估计系统状态参数的同时还需要对系统的运动模式进行辨识，所以不可能通过建立起一个固定的模型对系统状态进行效果较好的估计。针对这一问题，多模型的估计方法提出通过一个模型集{}(),1,2,,j M m j r == 中不同模型的切换来匹配不同目标的运动或者同一目标不同阶段的运动，达到运动模式的实时辨识的效果。 目前主要的多模型方法包括一阶广义贝叶斯方法（BGP1），二阶广义贝叶斯方法（GPB2）以及交互式多模型方法等（IMM ）。这些多模型方法的共同点是基于马尔科夫链对各自的模型集进行切换或者融合，他们的主要设计流程如下图： M={m1,m2,...mk} K 时刻输入 值的形式 图一 多模型设计方法 其中，滤波器的重初始化方式是区分不同多模型算法的主要标准。由于多模型方法都是基于一个马尔科夫链来切换与模型的，对于元素为r 的模型集{}(),1,2,,j M m j r == ，从0时刻到k 时刻，其可能的模型切换轨迹为 120,12{,,}k i i i k trace k M m m m = ，其中k i k m 表示K-1到K 时刻，模型切换到第k i 个， k i 可取1,2,,r ，即0,k trace M 总共有k r 种可能。再令1 2 1 ,,,,k k i i i i μ+ 为K+1时刻经由轨迹0,k trace M 输入到第1k i +个模型滤波器的加权系数，则输入可以表示为 0,11 2 1 12|,,,,|,,,???k k trace k k k i M k k i i i i k k i i i x x μ++=?∑ 可见轨迹0,k trace M 的复杂度直接影响到算法计算量和存储量。虽然全轨迹的
电气设计的负荷计算方法及其应用范围 电气负荷计算方法有:需要系数法,利用系数法,二项式系数法,单位面积功率计算法,单位产品功率计算法等. (1),需要系数法:用设备功率乘以需要系数和同时系数,直接求出计算负荷; (2),利用系数法:采用利用系数求出最大负荷班的平均负荷,再考虑设备台娄和功率差异的影响,乘以与有效台数有关的最大系数求得计算负荷; (3),二项式系数法:将负荷分为基本负荷和附加负荷,后者考虑一定数量大容量设备影响; (4),单位面积功率法,单位指标法,单位产品耗电量法等,可用于初步设计用电量指标的估算,对于住宅建筑,在设计各阶段均可使用单位面积功率法. 它们的应用范围各不一样,按《民用建筑电气设计规范》3.4.2.1."在方案设计阶段可采用单位指标法;在初步设计阶段,宜采用需要系数法."可见:民用建筑电气计算负荷推荐采用需要系数法;这是因为民用建筑中电气设备很少有特别突出的大功率设备,而按照需要系数法简单易行;而在工业建筑中,由于各设备的用电量存的很大差异,用需要系数法进行计算与实际就存在很大出入. 例如:某车间用电设备如下: 电焊机25台,功率分别 为:3.0KVA*8;8KVA*6;16KVA*5;30KVA*2;180KVA*2;200KVA*2;ε=50% 风机:50台,功率均为:2.2KW 机床:66台,功率分别为:7.5Kw*30;15KW*30;30KW*2;45KW*2;90KW*2 吊车:2台,分别为15KW,22KW. 本车间的总配电计算负荷用上述(1),(2),(3)分别如下: (一),采用需要系数法: 电焊机,Kx=0.35, Pjs=Kx*Pe =0.35*972**cosΦ =0.35*972**0.7=168.39Kw Qjs=Pjs*tgΦ=1.02*168.39=171.76Kvar 风机:Kx=0.75 Pjs=Kx*Pe=0.75*50*2.2=82.5KW Qjs=Pjs*tgΦ=0.75*82.5=61.9Kvar 机床:Kx=0.12 Pjs=Kx*Pe=0.12*1005=120.6KW Qjs=Pjs*tgΦ=1.73*120.65=208.6Kvar 吊车:Kx=0.1 Pjs=Kx*Pe=0.1*37=3.7KW Qjs=Pjs*tgΦ=1.73*3.7=6.4Kvar P∑=K∑p*∑Pjs=0.9*374.8=375.19KW Q∑=K∑q*∑Qjs=0.95*374.8=448.66KW S∑==584.86KVA cosΦ∑=0.505
?哈弗曼编码 A method for the construction of minimum-re-dundancy codes, 耿国华1数据结构1北京:高等教育出版社,2005:182—190 严蔚敏,吴伟民.数据结构(C语言版)[M].北京:清华大学出版社,1997. 冯桂,林其伟,陈东华.信息论与编码技术[M].北京:清华大学出版社,2007. 刘大有,唐海鹰,孙舒杨,等.数据结构[M].北京:高等教育出版社,2001 ?压缩实现 速度要求 为了让它(huffman.cpp)快速运行，同时不使用任何动态库，比如STL或者MFC。它压缩1M数据少于100ms（P3处理器，主频1G）。 压缩过程 压缩代码非常简单，首先用ASCII值初始化511个哈夫曼节点： CHuffmanNode nodes[511]; for(int nCount = 0; nCount < 256; nCount++) nodes[nCount].byAscii = nCount; 其次，计算在输入缓冲区数据中，每个ASCII码出现的频率： for(nCount = 0; nCount < nSrcLen; nCount++) nodes[pSrc[nCount]].nFrequency++; 然后，根据频率进行排序： qsort(nodes, 256, sizeof(CHuffmanNode), frequencyCompare); 哈夫曼树，获取每个ASCII码对应的位序列： int nNodeCount = GetHuffmanTree(nodes); 构造哈夫曼树 构造哈夫曼树非常简单，将所有的节点放到一个队列中，用一个节点替换两个频率最低的节点，新节点的频率就是这两个节点的频率之和。这样，新节点就是两个被替换节点的父
一. 三相用电设备组计算负荷的确定: 1. 单组用电设备负荷计 算: P30=KdPe Q30=P30tanφS30=P30/cosφI30=S30/(1.732UN) 2. 多组用电设备负荷计 算: P30=K∑p∑P30,i Q30=K∑q∑Q30,i S30=(P²30+Q& sup2;30)½ I30=S30/(1.732UN) 注: 对车间干线取K∑p=0.85～0.95 K∑q=0.85～ 0.97 对低压母线①由用电设备组计算负荷直接相加来计算时取? ?K∑p=0.80～0.90? ???K∑q=0.85～0.95? ?? ?? ?? ??? ? ?? ?? ?? ?? ?? ?? ?? ?? ?? ???②由车间干线计算负荷直接相加来计算时取? ???K∑p=0.90～0.95? ???K∑q=0.93～0.97? ?? ?? ?? ??? ? ?3. 对断续周期工作制的用电设备组? ???①电焊机组要求统一换算到ε=100﹪, Pe=PN(εN)½ =Sncosφ(εN)½ (PN.SN为电焊机的铭牌容 量;εN为与铭牌容量对应的负荷持续率;cosφ为铭牌规定的功率因数. ) ②吊车电动机组要求统一换算到ε=25﹪, Pe=2PN(εN)½ 二. 单相用电设备组计算负荷的确定: 单相设备接在三相线路中,应尽可能地均衡分配,使三相负荷尽可能的平衡.如果三相线路中单相设备的总容量不超过三相设备总容量的 15﹪,则不论单相设备容量如何分配,单相设备可与三相设备综合按三相负荷平衡计算.如果单相设备容量超过三相设备容量15﹪时,则应将 单相设备容量换算为等效三相设备容量,再与三相设备容量相加.
需要系数法计算负荷 1、车间变电所的计算负荷 有功、无功功率的同时系数分别取0.8-1、0.93-1 2、配电所或总降压变电所的计算负荷为各车间变电所计算负荷之和再乘以同时系数。配电所的同时系数分别为0.85-1、0.95-1，总降压变电所的同时系数分别为0.8-0.9、0.93-0.97. 单位面积功率法确定计算负荷 Pe=Pe'.S/1000 kW Pe----计算有功功率 Pe'----单位面积功率（负荷密度）w/m2（见下表1） S------建筑面积m2 用以上方法计算负荷时，还应结合工程具体情况，乘以不同的系数，系数见下表2 电能计算 W=η*Pe*365*24 η-----平均负荷系数，缺少经验数据时取0.25-0.35 民用建筑负荷密度指标（表1） 建筑类别负荷密度（w/m2) 住宅建筑 基本型50 提高型75 先进型100 公寓建筑30-50
旅馆建筑40-70 办公建筑30-70 商业建筑 一般40-80 大中型60-120 体育建筑40-70 剧场建筑50-80 医疗建筑40-70 教育建筑 大专院校20-40 中小学校12-20 展览建筑50-80 演播室250-500 汽车库8-15 住宅建筑用电负荷需要系数（表2） 户数系数户数系数 3 1 18 0.50 4 0.9 5 21 0.50 6 0.80 24 0.45 8 0.70 25-100 0.45 10 0.65 125-200 0.35 12 0.60 260-300 0.30 14 0.55 16 0.55
注：1、表中户数是指单相配电时接于同一相上的户数，按三相配电 时连接的户数应乘以3。 住宅的公用照明和电力负荷需要系数可按0.8选取。 国家发改委2010年10月出台的居民阶梯电价方案中： 居民生活阶梯电价全国平均电量分档标准表 第一档第二档第三档 全国平均分档标准 全国 平均 分档 标准 用户 覆盖 率 全国 平均 分档 标准 合计城市合计城市农村 % % % 度/月% % % 度/月% 70% 51% 79% 110 90% 82% 95 % 210 100 % 80% 65% 88% 140 95% 90% 98 % 270 100 % 天然气每户每月9-12立方米 管道煤气每户每月30立方米左右
LZ77压缩算法实验报告 一、实验内容 使用C++编程实现LZ77压缩算法的实现。 二、实验目的 用LZ77实现文件的压缩。 三、实验环境 1、软件环境：Visual C++ 6.0 2、编程语言：C++ 四、实验原理 LZ77 算法在某种意义上又可以称为“滑动窗口压缩”，这是由于该算法将一个虚拟的，可以跟随压缩进程滑动的窗口作为术语字典，要压缩的字符串如果在该窗口中出现，则输出其出现位置和长度。使用固定大小窗口进行术语匹配，而不是在所有已经编码的信息中匹配，是因为匹配算法的时间消耗往往很多，必须限制字典的大小才能保证算法的效率；随着压缩的进程滑动字典窗口，使其中总包含最近编码过的信息，是因为对大多数信息而言，要编码的字符串往往在最近的上下文中更容易找到匹配串。 五、LZ77算法的基本流程 1、从当前压缩位置开始，考察未编码的数据，并试图在滑动窗口中找出最长的匹 配字符串，如果找到，则进行步骤2，否则进行步骤3。 2、输出三元符号组( off, len, c )。其中off 为窗口中匹
配字符串相对窗口边 界的偏移，len 为可匹配的长度，c 为下一个字符。然后将窗口向后滑动len + 1 个字符，继续步骤1。 3、输出三元符号组( 0, 0, c )。其中c 为下一个字符。然后将窗口向后滑动 len + 1 个字符，继续步骤1。 六、源程序 /********************************************************************* * * Project description: * Lz77 compression/decompression algorithm. * *********************************************************************/ #include 负荷计算方法 供电设计常采用的电力负荷计算方法有：需用系数法、二项系数法、利用系数法和单位产品电耗法等。需用系数法计算简便，对于任何性质的企业负荷均适用，且计算结果基本上符合实际，尤其对各用电设备容量相差较小，且用电设备数量较多的用电设备组，因此，这种计算方法采用最广泛。二项系数法主要适用于各用电设备容量相差大的场合，如机械加工企业、煤矿井下综合机械化采煤工作面等。利用系数法以平均负荷作为计算的依据，利用概率论分析出最大负荷与平均负荷的关系，这种计算方法目前积累的实用数据不多，且计算步骤较繁琐，故工程应用较少。单位产品电耗法常用于方案设计。 一、设备容量的确定 用电设备铭牌上标出的功率（或称容量）称为用电设备的额定功率P N ，该功率是指用电设备（如电动机）额定的输出功率。 各用电设备，按其工作制分，有长期连续工作制、短时工作制和断续周期工作制三类。因而，在计算负荷时，不能将其额定功率简单地直接相加，而需将不同工作制的用电设备额定功率换算成统一规定的工作制条件下的功率，称之为用电设备功率P Nμ。 （一）长期连续工作制 这类工作制的用电设备长期连续运行，负荷比较稳定，如通风机、空气压缩机、水泵、电动发电机等。机床电动机，虽一般变动较大，但多数也是长期连续运行的。 对长期工作制的用电设备有 P Nμ=P N （2－9） （二）短时工作制 这类工作制的用电设备工作时间很短，而停歇时间相当长。如煤矿井下的排水泵等。 对这类用电设备也同样有 P Nμ=P N （2－10） （三）短时连续工作制用电设备 这类工作制的用电设备周期性地时而工作，时而停歇。如此反复运行，而工作周期一般不超过10分钟。如电焊机、吊车电动机等。断续周期工作制设备，可用“负荷持续率”来表征其工作性质。 负荷持续率为一个工作周期内工作时间与工作周期的百分比值，用ε表示 100%100%t t T t t ε=?=?+ （2－11） 式中 T ——工作周期，s ； t ——工作周期内的工作时间，s ； t 0——工作周期内的停歇时间，s 。 断续周期工作制设备的设备容量，一般是对应于某一标准负荷持续率的。 应该注意：同一用电设备，在不同的负荷持续率工作时，其输出功率是不同的。因此，不同负荷持续率的设备容量（铭牌容量）必须换算为同一负荷持续率下的容量才能进行相加运算。并且，这种换算应该是等效换算，即按同一周期内相同发热条件来进行换算。由于电流I 通过设备在t 时间内产生的热量为I 2Rt ，因此，在设备电阻不变而产生热量又相同的条 件下，I ∝P ∝I 。由式（2－11）可知，同一周期的负荷持 续率ε∝t 。因此，P ∝ε 实验名称：LZSS压缩算法实验报告 一、实验内容 使用Visual 6..0 C++编程实现LZ77压缩算法。 二、实验目的 用LZSS实现文件的压缩。 三、实验原理 LZSS压缩算法是词典编码无损压缩技术的一种。LZSS压缩算法的字典模型使用了自适应的方式，也就是说，将已经编码过的信息作为字典， 四、实验环境 1、软件环境：Visual C++ 6.0 2、编程语言：C++ 五、实验代码 #include -- need byte-swap function for big endian CPU */ #define READ_LE32(X) *(uint32_t *)(X) #define WRITE_LE32(X,Y) *(uint32_t *)(X) = (Y) /* this assumes sizeof(long)==4 */ typedef unsigned long uint32_t; /* text (input) size counter, code (output) size counter, and counter for reporting progress every 1K bytes */ static unsigned long g_text_size, g_code_size, g_print_count; /* ring buffer of size N, with extra F-1 bytes to facilitate string comparison */ static unsigned char g_ring_buffer[N + F - 1]; /* position and length of longest match; set by insert_node() */ static unsigned g_match_pos, g_match_len; /* left & right children & parent -- these constitute binary search tree */ static unsigned g_left_child[N + 1], g_right_child[N + 257], g_parent[N + 1]; /* input & output files */ static FILE *g_infile, *g_outfile; /***************************************************************************** initialize trees *****************************************************************************/ static void init_tree(void) { unsigned i; /* For i = 0 to N - 1, g_right_child[i] and g_left_child[i] will be the right and left children of node i. These nodes need not be initialized. Also, g_parent[i] is the parent of node i. These are initialized to NIL (= N), which stands for 'not used.' For i = 0 to 255, g_right_child[N + i + 1] is the root of the tree for strings that begin with character i. These are initialized to NIL. Note there are 256 trees. */ for(i = N + 1; i <= N + 256; i++) g_right_child[i] = NIL; for(i = 0; i < N; i++) g_parent[i] = NIL; } /***************************************************************************** Inserts string of length F, g_ring_buffer[r..r+F-1], into one of the trees (g_ring_buffer[r]'th tree) and returns the longest-match position and length via the global variables g_match_pos and g_match_len. If g_match_len = F, then removes the old node in favor of the new one, because the old one will be deleted sooner. 牛津高阶英汉双解词典 英语谚语、格言和警句_Sayings and Proverbs 谚语、格言和警句蕴藏着人类的智慧，简短的词句、诙谐的表达中流露着人生的哲理。我希望能和大家一同分享这些睿智的语言，体悟语言世界的奥妙。现计划每天摘录一句，并附解释。如果大家对其中的某些妙句有兴趣和想法，欢迎大家针对性地发表意见；同时欢迎大家收集英语谚语、格言和警句在本贴发布，以供大家分享，最好注明出处。希望与大家快乐而智慧地共同进步。 我目前所摘的谚语、格言和警均来自《牛津高阶英汉双解词典》（第七版）【商务印书馆，牛津大学出版社（中国）有限公司，2009年4月第3版】 Below is a list of well-known sayings and proverbs: fixed phrases or sentences that give advice or say something that is generally true. 下列广泛流传的谚语、格言和警句，即给予劝戒或表达普遍真理的固定短语或句子。 1—absence makes the heart grow fonder ?英文解释：used to say that when you are away from sb that you love, you love them even more ?翻译：不相见，倍思念 2—there's no accounting for taste ?英文解释：used to say how difficult it is to understand why sb likes sb/sth that you do not like at all ?翻译：人的爱憎好恶是无法解释的；人各有所好 3—actions speak louder than words ?英文解释：what a person actually does means more than what they say they will do ?翻译：行动比言语更为响亮 4—it'll be all ringt on the night ?英文解释：used to say that a performance, an event, etc. will be successful even if the preparations for it have not gone well ?翻译：（演出、活动等）到时候自会成功的；车到山前必有路 5—the apple doesn't fall far from the tree OR—the apple never falls far from the tree ?英文解释：a child usually behaves in a similar way to his or her parent(s) ?翻译：有什么样的父母就有什么样的儿女；上行下效 多媒体数据压缩实验报告 篇一：多媒体实验报告_文件压缩 课程设计报告 实验题目：文件压缩程序 姓名：指导教师：学院：计算机学院专业：计算机科学与技术学号： 提交报告时间：20年月日 四川大学 一，需求分析： 有两种形式的重复存在于计算机数据中，文件压缩程序就是对这两种重复进行了压 缩。 一种是短语形式的重复，即三个字节以上的重复，对于这种重复，压缩程序用两个数字：1.重复位置距当前压缩位置的距离；2.重复的长度，来表示这个重复，假设这两个数字各占一个字节，于是数据便得到了压缩。 第二种重复为单字节的重复，一个字节只有256种可能的取值，所以这种重复是必然的。给 256 种字节取值重新编码，使出现较多的字节使用较短的编码，出现较少的字节使用较长的编码，这样一来，变短的字节相对于变长的字节更多，文件的总长度就会减少，并且，字节使用比例越不均 匀，压缩比例就越大。 编码式压缩必须在短语式压缩之后进行，因为编码式压缩后，原先八位二进制值的字节就被破坏了，这样文件中短语式重复的倾向也会被破坏（除非先进行解码）。另外，短语式压缩后的结果：那些剩下的未被匹配的单、双字节和得到匹配的距离、长度值仍然具有取值分布不均匀性，因此，两种压缩方式的顺序不能变。 本程序设计只做了编码式压缩，采用Huffman编码进行压缩和解压缩。Huffman编码是一种可变长编码方式，是二叉树的一种特殊转化形式。编码的原理是：将使用次数多的代码转换成长度较短的代码，而使用次数少的可以使用较长的编码，并且保持编码的唯一可解性。根据 ascii 码文件中各 ascii 字符出现的频率情况创建 Huffman 树，再将各字符对应的哈夫曼编码写入文件中。同时，亦可根据对应的哈夫曼树，将哈夫曼编码文件解压成字符文件. 一、概要设计： 压缩过程的实现: 压缩过程的流程是清晰而简单的: 1. 创建 Huffman 树 2. 打开需压缩文件 3. 将需压缩文件中的每个 ascii 码对应的 huffman 编码按 bit 单位输出生成压缩文件压缩结束。 The Oxford 3000 wordlist ?a, an indefinite article ?abandon v. ?abandoned adj. ?ability n. ?able adj. ?unable adj. ?about adv.,prep. ?above prep.,adv. ?abroad adv. ?absence n. ?absent adj. ?absolute adj. ?absolutely adv. ?absorb v. ?abuse n.,v. ?academic adj. ?accent n. ?accept v. ?acceptable adj. ?unacceptable adj. ?access n. ?accident n. ?by accident ?accidental adj. ?accidentally adv. ?accommodation n.?accompany v. ?according to prep. ?account n.,v. ?accurate adj. ?accurately adv. ?accuse v. ?achieve v. ?achievement n. ?acid n. ?acknowledge v. ?acquire v.?across adv.,prep. ?act n.,v. ?action n. ?take action ?active adj. ?actively adv. ?activity n. ?actor, actress n. ?actual adj. ?actually adv. ? ad advertisement ?adapt v. ?add v. ?addition n. ?in addition (to) ?additional adj. ?address n.,v. ?adequate adj. ?adequately adv. ?adjust v. ?admiration n. ?admire v. ?admit v. ?adopt v. ?adult n.,adj. ?advance n.,v. ?advanced adj. ?in advance ?advantage n. ?take advantage of ?adventure n. ?advertise v. ?advertising n. ?advertisement (also ad, advert) n.?advice n. ?advise v. ?affair n. ?affect v. 需要系数法确定计算负荷 7.4需要系数法确定计算负荷 7.4.1基本公式 用电设备组的计算负荷是指用电设备组从供电系统中取用的半小时最大负荷P30，如图7-5所示。用电设备组的设备容量Pe是指设备组内全部设备(不包括备用设备)的额定容量之和，即Pe＝ΣPN。当设备的暂载率不是100％时，需要折合以后的设备容量。设备的额定容量是指设备在额定条件下的最大输出功率。实际上，当用电设备数量较多时，用电设备组的设备极少同时运行，而运行的设备也不一定是都处在满负荷运行状态下。再加上，设备本身有功率的损耗和配电线路上的损耗，用电设备组的有功计算公式应考虑这些因素。图7-5中Kx称为需要系数，它由公式(7-16)中多个参数决定。 图7-5用电设备组的计算负荷与设备的容量 P30＝KΣ·KL────ηe·ηWL·Pe(7-16) 式中：KΣ──设备组的同期系数，即设备组在最大负荷时运行的设备容量与全部设备容量之和的比值。 KL──设备组的负荷系数，即设备组在最大负荷时的输出功率与运行设备容量之比。 ηe──设备组的平均效率，即设备组在实际运行最大负荷时的输出功率与取用功率之比。 ηWL──配电线路的平均效率，即配电线路在最大负荷时的末端功率(设备组的取用电功率)和首端输入电功 率(计算负荷)之比。 Pe──是指经过暂载率折合以后的计算功率。 令KΣ·KL/ηe·ηWL＝Kx Kx＝P30/Pe (7-17) 用电设备组的需要系数，就是用电设备组在最大负荷时需要的有功功率与其设备容量的比值，一般小于1。 由此可得按需要系数法确定三相用电设备组的有功功率的基本公式为： P30＝Kx·Pe(7-18) 实际上，需要系数不仅与用电设备组的工作性质(连续运行否)、设备台数、设备效率、线路损耗等原因有关，而且与设备的运行频繁程度、供电组织等多种因素有关。 7.4.2需要系数的参考数值 表7-3列出了不同用电设备的需要系数。 工业用电设备组的需要系数Kx、二项式系数bc及cosφ值表7-3 价值工程 置，是一项十分有意义的工作。另外恶意代码的检测和分析是一个长期的过程，应对其新的特征和发展趋势作进一步研究，建立完善的分析库。 参考文献： [1]CNCERT/CC.https://www.doczj.com/doc/7212174367.html,/publish/main/46/index.html. [2]LO R,LEVITTK,OL SSONN R.MFC:a malicious code filter [J].Computer and Security,1995,14(6):541-566. [3]KA SP ER SKY L.The evolution of technologies used to detect malicious code [M].Moscow:Kaspersky Lap,2007. [4]LC Briand,J Feng,Y Labiche.Experimenting with Genetic Algorithms and Coupling Measures to devise optimal integration test orders.Software Engineering with Computational Intelligence,Kluwer,2003. [5]Steven A.Hofmeyr,Stephanie Forrest,Anil Somayaji.Intrusion Detection using Sequences of System calls.Journal of Computer Security Vol,Jun.1998. [6]李华,刘智,覃征,张小松.基于行为分析和特征码的恶意代码检测技术[J].计算机应用研究，2011，28（3）：1127-1129. [7]刘威，刘鑫，杜振华.2010年我国恶意代码新特点的研究.第26次全国计算机安全学术交流会论文集，2011，（09）. [8]IDIKA N,MATHUR A P.A Survey of Malware Detection Techniques [R].Tehnical Report,Department of Computer Science,Purdue University,2007. 0引言 现有的压缩算法有很多种，但是都存在一定的局限性，比如：LZw [1]。主要是针对数据量较大的图像之类的进行压缩，不适合对简单报文的压缩。比如说，传输中有长度限制的数据，而实际传输的数据大于限制传输的数据长度，总体数据长度在100字节左右，此时使用一些流行算法反而达不到压缩的目的，甚至增大数据的长度。本文假设该批数据为纯数字数据，实现压缩并解压缩算法。 1数据压缩概念 数据压缩是指在不丢失信息的前提下，缩减数据量以减少存储空间，提高其传输、存储和处理效率的一种技术方法。或按照一定的算法对数据进行重新组织，减少数据的冗余和存储的空间。常用的压缩方式[2,3]有统计编码、预测编码、变换编码和混合编码等。统计编码包含哈夫曼编码、算术编码、游程编码、字典编码等。 2常见几种压缩算法的比较2.1霍夫曼编码压缩[4]：也是一种常用的压缩方法。其基本原理是频繁使用的数据用较短的代码代替，很少使用 的数据用较长的代码代替，每个数据的代码各不相同。这些代码都是二进制码，且码的长度是可变的。 2.2LZW 压缩方法[5,6]：LZW 压缩技术比其它大多数压缩技术都复杂，压缩效率也较高。其基本原理是把每一个第一次出现的字符串用一个数值来编码，在还原程序中再将这个数值还成原来的字符串，如用数值0x100代替字符串ccddeee"这样每当出现该字符串时，都用0x100代替，起到了压缩的作用。 3简单报文数据压缩算法及实现 3.1算法的基本思想数字0-9在内存中占用的位最 大为4bit ， 而一个字节有8个bit ，显然一个字节至少可以保存两个数字，而一个字符型的数字在内存中是占用一个字节的，那么就可以实现2:1的压缩，压缩算法有几种，比如，一个自己的高四位保存一个数字，低四位保存另外一个数字，或者，一组数字字符可以转换为一个n 字节的数值。N 为C 语言某种数值类型的所占的字节长度，本文讨论后一种算法的实现。 3.2算法步骤 ①确定一种C 语言的数值类型。 —————————————————————— —作者简介：安建梅（1981-），女，山西忻州人，助理实验室，研究方 向为软件开发与软交换技术；季松华（1978-），男，江苏 南通人，高级软件工程师，研究方向为软件开发。 数据快速压缩算法的研究以及C 语言实现 The Study of Data Compression and Encryption Algorithm and Realization with C Language 安建梅①AN Jian-mei ；季松华②JI Song-hua （①重庆文理学院软件工程学院，永川402160；②中信网络科技股份有限公司，重庆400000）（①The Software Engineering Institute of Chongqing University of Arts and Sciences ，Chongqing 402160，China ； ②CITIC Application Service Provider Co.，Ltd.，Chongqing 400000，China ） 摘要：压缩算法有很多种，但是对需要压缩到一定长度的简单的报文进行处理时，现有的算法不仅达不到目的，并且变得复杂， 本文针对目前一些企业的需要，实现了对简单报文的压缩加密，此算法不仅可以快速对几十上百位的数据进行压缩，而且通过不断 的优化，解决了由于各种情况引发的解密错误，在解密的过程中不会出现任何差错。 Abstract:Although,there are many kinds of compression algorithm,the need for encryption and compression of a length of a simple message processing,the existing algorithm is not only counterproductive,but also complicated.To some enterprises need,this paper realizes the simple message of compression and encryption.This algorithm can not only fast for tens of hundreds of data compression,but also,solve the various conditions triggered by decryption errors through continuous optimization;therefore,the decryption process does not appear in any error. 关键词：压缩；解压缩；数字字符；简单报文Key words:compression ；decompression ；encryption ；message 中图分类号：TP39文献标识码：A 文章编号：1006-4311（2012）35-0192-02 ·192·负荷计算方法
LZSS压缩算法实验报告
牛津高阶英汉词典-英语谚语(新)
多媒体数据压缩实验报告
牛津字典核心3000词汇(The+Oxford+3000+wordlist)
需要系数法确定计算负荷
数据快速压缩算法的C语言实现
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