JPS60113124A - Spectrum data interpolating device - Google Patents

Abstract

PURPOSE:To use effectively a CPU by performing the interpolating processing of spectrum data in a short time on a basis of the method of weighed moving arranges of minimum square-law adaptation type. CONSTITUTION:Two actually measured data before and after an interpolation object within a minimum square-law adaptation width are inputted to a temporary interpolating value calculator 2 to calculate a temporary interpolating value, and actually measured data within the minimum square-law adaptation width and coefficients of weight which are allowed to correspond to actually measured data on a basis of the minimum square-law adaptation width are inputted to a multiplier 4, and both of them are multiplied. Plural multipliers 4 are provided in accordance with the set minimum square-law adaptation width. Multiplied values of individual multipliers 4 are added by an adder 6. The added value from the adder 6 and a coefficient of normalization based on the minimum square-low adaptation width are inputted to a divider 8, and the added value is divided by the coefficient of normalization to calculate an interpolating value.

Description

DETAILED DESCRIPTION OF THE INVENTION (a) Industrial application field The present invention relates to spectral data used when interpolating between each measured data of spectral data obtained by photoelectron spectroscopy, emission spectroscopy, X-ray analysis, etc. Regarding an interpolation device.

(CI) Prior Art Generally, when spectral data obtained from photoelectron spectroscopy, emission spectroscopy, X-ray analysis, etc. is output to a display such as an XY plotter or CRT, it is displayed as a distribution curve containing many peaks. By the way, there are cases where it is desired to enlarge and display the obtained spectral data during analysis. In such cases, a method of interpolating between actually measured data may be used to facilitate observation. Conventionally, in order to interpolate between each data, first, interpolated data is sequentially calculated by 4 using spectrum data obtained by measurement. However, in the case of data processing that relies on the least squares method, if there is a large number of spectral data, it takes a long calculation time, and there are problems in that not only is it not possible to immediately enlarge the display, but also it is not possible to make effective use of the CPU.

(c) In order to achieve the above object, the present invention aims to enable data capture processing to be performed in a shorter time than in the past, thereby making it possible to utilize the CPU more effectively.

(d) Structure In order to achieve the above object, the present invention interpolates spectral data based on the moving average method with least squares adaptive weighting, which is one of the data flattening processes. . That is, the present invention provides a temporary correction value calculator that calculates a provisional interpolation value by inputting two measured data before and after an interpolation A plurality of multipliers are inputted with weighting coefficients respectively associated with the actual profit data based on this least squares adaptation width and multiplied by both, and multiplier values output from each of these multipliers are inputted and added together. an adder;
A spectral data interpolation device comprising: a divider that inputs the added value output from the adder and a normalization coefficient based on the least squares adaptation width and divides the added value by the normalization coefficient.

(E) Example Hereinafter, the present invention will be explained in detail based on an example shown in the drawings.

FIG. 1 is a block diagram of the spectral data interpolation device of this embodiment. In the figure, numeral 1 is a spectral data interpolation device, and 2 is a spectral data interpolation device that inputs two actual measured data before and after the interpolation target within the least squares fitting width and calculates a provisional interpolation value (in this example, the average value between two points). 4 is a multiplier that inputs the measured data within the least squares fitting width and the weighting coefficients respectively associated with the above measured data based on this least squares fitting width and dilutes both. A plurality of p-devices 4 are provided to match the set least squares adaptation width. 6 is an adder that inputs the multiplication values output from each multiplier 4 and adds them; 8 inputs the addition value output from this adder 6 and a normalization coefficient based on the least squares adaptation width; This is a divider that calculates an interpolated value by dividing the added value by the normalization coefficient.

Next, a data interpolation operation in the spectral data interpolation device 1 having the above configuration will be explained.

Spectral data measured by a photoelectron spectrometer, an X-ray analyzer, etc. is taken in and stored in a CPU (not shown). Roughly, the CPU is based on the least squares fitting type weighted moving average method (the parameters of the least squares fitting width, the weight coefficients according to this least squares fitting width, and the normalization coefficient data are stored in advance. From this state, if we then set the parameter P (P is an integer) in the CPU, the least squares fitting width 2P+1 is determined at the same time, and the weighting coefficient W and normalization coefficient N that follow this least squares fitting width 2P+1 are It is set uniquely (step 1).The CPU then sets the least squares fitting width 2P+ as shown in Fig. 2.
Two actual im data Dn before and after the interpolation target within 1,
Dn++ is output to the temporary interpolation value calculator 2 of the spectral data interpolation device 1. The temporary interpolation value calculator 2 calculates the temporary interpolation value Do using the following equation based on the two actual measurement data Dn and Dn-+-+ (step 2).

Do=11 The calculated temporary interpolation value DO is sent to the next stage multiplier 4 connected to this temporary interpolation value calculator 2. At the same time, CP
From U to each multiplier 4 corresponding to the least squares fitting width 2P+1, the actual measurement 7' -9Dn within the least squares fitting width 2P+1
−(1)−1)−・=Dn, Dn−+−+−・−D
n+p and this least square fitting width 2P+1, the weighting coefficient W associated with the above-mentioned measured data DIM p-+ )...Dn, Dn+...D(n+p)
-p...W-+, Wo, W+...Wp
are output respectively. Each multiplication hole 4 has input actual measurement data Dn −< p −1) . . . −Dn, Dn+
+−・−・Dn+p and weighting coefficients W−po, W−+,
Wo, W+...Wp are multiplied by the following formula (multiplying values X-p...X-1, X+, X+...
...Calculate Xp (step 3).

X-p = Dn-< p-1)・W-pχ-+=D
n-W-+Xo=Do.W.

xl-Dl・WI Xp = Dn+p-Wp Multiply value x-p calculated by each multiplier 4
s, Xo.

xl...X]) li are both output to the adder 6 at the next stage. The adder 6 receives the multiplied value X-p from each multiplier 4.
. . X-1°Xo, X+ . . . Xp are added to calculate the added value Y as shown in the following equation.

Y=X−p+ ・−−−−−X−++)(o+X++
°°...=+Xp This added value Y is sent to the divider 8 at the next stage. At this time, the CPU simultaneously sends a normalization coefficient N to the divider 8 based on the least squares fitting width 2P+1.

The divider 8 divides the manually added value Y by the normalization coefficient N to calculate the interpolated value pH+o,s as shown in the following equation (step 5).

Dn + o, s = Y/ N When the interpolation value 1) n + o, s between the two actual measurement data Dn, Dn++ is calculated as described above, the CPU then shifts the actual measurement data string by one and calculates the interpolation value between the two actual measurement data Dn, Dn++. The value calculator 2 receives the next measured data D(n++) within the least squares fitting width 2P+1.

D(n+2), and each multiplier 4 has D(n++)
=(1)-1) to D(n+n+p) are output respectively.Then, the operations from step 2 to step 5 are repeated in the same way to obtain the next interpolated value D(n+n+o, s
Calculate.

In this way, the operations from step 2 to step 5 are sequentially repeated to interpolate between the actually measured data. Therefore,
Once the least squares fitting width 2P+1 is set, a total of 4 steps from Step 2 to Step 5 are required to calculate one interpolated value.
The time required for the calculation is extremely short since the processing in step 7 to 1 is sufficient.

In this embodiment, although the case of single-point interpolation has been described, it is possible to interpolate multiple points by repeating the interpolation process. Furthermore, if linear interpolation is performed between the measured data Dn and Dn++, a plurality of temporary interpolation points are set, and a least squares adaptation width is selected accordingly, it is possible to interpolate a plurality of points at once. Further, the present device can be widely applied not only to interpolation of spectral data but also to interpolation of data to which the conventional least squares method can be applied.

(f) Effects As described above, according to the present invention, interpolation processing of spectral data can be performed in a shorter time than in the past, making it easier to enlarge and display data, and moreover, the CPU can be used more effectively. This is an excellent effect.

[Brief explanation of drawings]

The drawings show an embodiment of the present invention, and FIG. 1 is a block diagram of a spectral data interpolation device, and FIG. 2 is an explanatory diagram of data interpolation processing. 1...spectral data interpolation device, 2...group...
Temporary sleeve distance value calculator, 4... Multiplier, 6...
・Adder, 8...Divider. Applicant Shimadzu Corporation Representative Patent Attorney 1) Hide Kazu

Claims (1)

[Claims] (1) A provisional interpolation value calculator that calculates a provisional interpolation value by inputting two measured data before and after the interpolation target within the least squares matching width, and the actual measured data within the least squares matching width and this least squares matching width. a plurality of multipliers that input weighting coefficients respectively associated with the measured data based on the above-mentioned data and multiply the two; an adder that inputs the multiplication values output from each of these multipliers and adds them together; A spectral data interpolation device comprising: a divider that inputs an added value output from an adder and a normalization coefficient based on the least squares adaptation width and divides the added value by the normalization coefficient.