Neural Network Based Sensor Fusion Strategy for Multi-Axis Machine Accuracy Monitoring and Control
Neural Network Based Sensor Fusion Strategy for Multi-Axis Machine Accuracy Monitoring and Control
Abderrazak El Ouafi* and Abdellah Bedrouni**
* Mathematics, Computer and Engineering
Department, University of Quebec at Rimouski, Canada.
**Mechanical Engineering Department
Laval University, Quebec, Canada.
ABSTRACT.This paper describes the initial phase of a major research work devoted to the investigation of a number of factors influencing the accuracy of machine tools. The investigation is indeed conducted so as to develop methods capable of providing practical solutions to the well-known machine tool loss of accuracy phenomenon.
Based on a multi-sensor monitoring system, a novel software error-compensation approach is proposed to provide the ability to predict geometric, thermal and dynamic errors and, hence, offer the possibility to improve the accuracy of multi-axis machines. The proposed method can be divided into the following steps: (i) design of a geometric model describing a multi-axis machine, (ii) measurement of path-dependent rigid-body errors according to the model, (iii) fusion of sensors and the measured parametric errors via an artificial neural network model (ANN), (iv) one-line prediction and integration of the individual errors to produce a correction vector, (v) compensation of imbedded errors. Implemented on a turning centre, the approach led to a substantial improvement of the machining accuracy characterised by a reduction of the maximum error from 70 μm to less than 4 μm.
K EYWORDS: Multi-axis machines, Error compensation, Sensor fusion, Neural network. 1. Introduction
Recent developments in production technology display the ever-increasing need for machine tools capable of achieving higher throughput and extreme accurate machining. In response to this on-going challenge facing the manufacturing industry, research efforts are directed towards the development and implementation of new cost-effective and practical methods that allow substantial improvements in the accuracy, speed, and repeatability of machine tools.
These efforts have generally focused on enhancing the positioning accuracy of CNC machine tools through process monitoring and on-line software error prediction and correction. The concept of error correction is based on monitoring the parameters of the machine tool and using the real-time data to maintain the desired machining process accuracy. The concept requires the development and
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implementation of a sensor fusion technique combined to a machine tool model that represents the interactions of the parameters being monitored.
Indeed, the performance of a machine tool is assessed according to its capability to accurately position the cutting tool. Obviously, each element involved in positioning the cutting tool with respect to the workpiece contributes to the resultant accuracy of the complete system. Resulting from various disturbances, errors in the positioning of the cutting tool affect the metal-removal process and introduce unacceptable deviations. These errors are generally classified according to their source and behaviour in the time domain [HOC 77].
Considered as slowly varying in time, quasi-static errors associated to the machine tool structure are due to imperfect geometry and kinematics of moving components, static deflections, and thermal distortions. On the other hand, dynamic errors are related to tool wear, tool chatter, spindle run-out, machine self-induced and forced vibrations, and other disturbances associated to the machining process. Although the dynamic errors are also important, the quasi-static errors are considered to be responsible for a very large proportion of the machine inaccuracy, contributing as much as 70% of the total positioning error [RAG 85].
Specific efforts in the area of machine tool metrology have focused on developing error modelling and prediction software [ZHA 85], [DON 86] and supporting the implementation of standard performance evaluation tests [BRY 82]. Several approaches to enhancing machining accuracy have been proposed. The conventional approach is based rigid body kinematics for modelling the errors in machine elements ([ZHA 85] and [FER 86]). Other alternative methods consist in using empirical models [BEL 87] or homogeneous transformation matrices [DON 86] to represent the final observed volumetric error in the workspace of a multi-axes machine. Early research in the area of machine tool metrology has concentrated on applying methods based on analytic [POR 80], trigonometric [LOV 73], vectorial [SCH 77] and [HOC 77] and matrix ([ZHA 85] and [FER 86]) error representations of multi-axis machines. More recent attempts propose the use of artificial intelligence concepts to estimate errors induced in a multi-axes machine[SRI 92].
The approach proposed in this paper is based on an accuracy-monitoring scheme designed to improve multi-axis machine performance by compensating for geometric, thermal, load-induced, and inertial errors. The essential feature of the monitoring system consists of measuring and modelling the individual errors through an ANN based multi-sensor fusion technique combined to a time-variant and spatial-variant position error kinematic integration model.
2. The proposed compensation system
2.1. On-line error compensation system
ANN Based Sensor Fusion Strategy for Multi-Axis Machine Accuracy Monitoring and Control 3 As noted above, the accuracy of a multi-axis machine is adversely affected by various error sources such as geometric imperfections, thermal deformations, load effects, and dynamic disturbances. The implementation of the concept of error compensation as a way to achieve accuracy improvements of a machine tool requires the need to ensure on-line evaluation or prediction of individual error components. Attempts to conduct on-line measurements of the parametric errors uncovered the difficulties and problems involved with integrating and using optical instruments in a hostile machining environment. Hence, in the absence of reliable, accurate, and hardened sensors and measuring devices, indirect methods provide an effective and continuous real-time error correction task that prevents defective parts from being machined. The application of artificial intelligence methods to a machine tool error compensation system offers, in addition, the opportunity to allow integration and faster processing of multiple sensor signals and enhance error prediction accuracy.
As illustrated in Figure 1, the proposed compensation scheme is designed to provide the ability to monitor the machine tool parameters (tool nominal position, cutting forces, temperature at various positions on the structure, speed and feedrate) and use the real-time data to control the accuracy of the machining process. The error-correction technique is implemented on a 285 x 1090 Mori Seiki SL 25 SE turning center. The sensors monitor the machine parameters and provide continuously the data that a multi-layer feedforward ANN uses to generate the individual error components. These errors are synthesized through a kinematic model to provide a correction vector that is fed into the servo loop of the machine to adjust the machining process accuracy.
Figure 1. Schematic diagram of accuracy monitoring system
2.2. Error integration model
In a typical machine tool, error is the difference between the actual and the anticipated response of the machine to a command issued according to the machine's accepted protocol [HOC 77]. This error results in a deviation of the
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cutting tool tip from the desired trajectory. The components of the positioning error of a two-axis turning center are shown in Figure 2. Assuming that the structural components of the machine tool are rigid bodies, the resultant error at the tool tip can also be described by a combination of individual displacement and rotational path-dependant errors.
A first step in the development of a compensation scheme consists, as stated above, in establishing a model in order to estimate the resultant error and hence derive the correction vector from the interaction in space of the individual error components. For this purpose, a general model providing the ability to estimate the correction vector has been developed from four sub-models: the basic geometric model, the coordinate system thermal drift model, the spindle error model, and the dynamic model. Then, the general model is used to define the necessary task of measuring the error components.
Figure 2. The resultant error component at the tip of the cutting tool
Basic geometric model
The individual errors are defined with respect to a single reference point representing the zero of the machine tool. The error characteristics are obtained by moving the carriage in the Z-axis and the cross slide in the X-axis. Displacement of the cutting edge from the machine original position [X o, Z o] to a nominal position [X n, Z n] introduces the geometric errors [?x, ?z]. As illustrated in Figure 3, these position-dependent errors resulting from the displacement of the machine components are obtained through a combination of the individual errors representing the linear, the straightness, the angular and the non-orthogonal errors induced along a machine axes.
Coordinate system thermal drift model
The second part of the general model intends to take into account the problem of the coordinate system drift due to the thermal disturbances. Assuming linear effects, the error vector ?d =δd x, δd y, δd z T is defined as the thermal drift. Obtained at
ANN Based Sensor Fusion Strategy for Multi-Axis Machine Accuracy Monitoring and Control 5
various machine thermal conditions, the thermal drift vector is added to the error vector derived from the geometric model.
Figure 3. Schematic diagram of a two-axis turning center Spindle error model
The third model is established to allow the derivation of the spindle thermal drift.In a turning center, three components associated to the spindle thermal drift are
critical to the machine accuracy. The first component is the axial thermal drift δs z
responsible for a displacement along the Z-axis. The second represents the radial thermal drift δs x
acting in a direction perpendicular to the Z-axis. The third
component is associated to the tilt thermal drift εs y
representing the angular deviation of the spindle axis in the X-Z plane.Dynamic error model
The fourth model is developed to allow the computation of the dynamic effects on the machine tool accuracy. The dynamic effects include herein two categories of
error sources. The error vector ?f =δf x , δf y , δf z T
defines the first category representing
the cutting force effects. The vector ?i =δi x , δi y , δi z T
represents the second category associated to the inertial effects.
As already mentioned, the components required to build the sub-models and consequently the general model are determined at various machine tool thermal states. The error components are obtained in terms of the machine tool measured parameters and operating conditions. As can be noticed, the absence of time as a variable is created by the need to simplify the modelling procedure. The use of time as a variable is susceptible of unnecessarily complicating the modeling procedure.From this point of view, the resultant error is, as illustrated in Figure 2, synthesized as follows:
y
s
p y z y x o x i x f x z x x x s x d Z )(Z x ε+ε+ε?δ?δ?δ?δ?δ+δ=?
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y
s
p y z y x o y z z x z i z f z z z s z d X )(X X z ε?ε+ε+ε+δ?δ?δ?δ?δ+δ=?The algorithm relative to the implementation of this model is schematically illustrated in Figure 4. Process sensing devices monitoring the nominal positions of the machine slides, temperature at various positions on the machine structure,cutting forces, spindle speed, and feed-rate generate signals that are scanned at a constant sampling rate. At every sample, each individual error is predicted using the appropriate model.
Figure 4. The bloc diagram of the integration scheme 2.3. The proposed sensor fusion method
Prediction of the individual errors enables the evaluation of the resultant error components at any location within the machine tool working space using the multiple variable models. These models are developed to include all factors contributing to the deviation of the cutting tool from the desired trajectory.Since error sources exhibit highly non-linear interactions with the machine conditions, a precise quantitative prediction of the individual errors is difficult to achieve using theoretical analysis. Indeed, on-line individual error evaluation through a kind of multiple-input/output empirical model allows for faster
ANN Based Sensor Fusion Strategy for Multi-Axis Machine Accuracy Monitoring and Control 7 processing and enhanced prediction accuracy. The implementation of this point of view comes up against the following difficulties: (i) choice of the modeling technique and (ii) selection of the predictor variables.
Model building analysis is often conducted using a large set of variables. From these candidate predictor variables, only an optimal subset is indeed useful for predicting the response. The identification of important input variables is crucial to the success of any empirical model. In this study, a systematic procedure designed for model building is presented.
Compared to the GMDH and the Least-Squares Regression Technique, neural networks provide a more effective modeling capability for predicting the error components. This is particularly true when the relationship between the sensor-based information and the actual error is non-linear. Based on various selection criteria, the selection of the candidate predictor variables can be achieved using some statistical techniques.
Neural network analysis
Artificial neural network models are used to express the geometric path-dependant errors as a function of the cutting tool nominal position, temperature, cutting forces, speed and feed-rate. This approach offers the ability to model and generalise without overfitting highly non-linear relationships. The use of the ANN model can further significantly reduce measurement and off-line calibration efforts.
The ANN model used is a multilayered Perceptron involving a collection of simple interconnected non-linear processing elements. The elements or neural nodes are arranged in patterns and operate in parallel. Using the well-known backpropagation technique and the generalised delta rule [MCC 88], the multilayer Perceptron is trained to find an acceptable weight solution.
Variables selection procedure
The idea behind the sensor selection using statistical techniques is based on the comparison of a complete model containing all predictor variables and a model with reduced number of input variables. This procedure can be implemented as follows:
i)Train a sufficient number of fusion models. Each model should be designed
with a subset of input sensors selected randomly.
ii)Compute the overall performance of each model under development according to the selection criterion.
iii)Determine the contribution of each sensor to the overall performance using appropriate statistical tools.
Many statistical criteria can be used to assess whether a reduced model is an adequate representation of the relationship between model response and predictor variables. The performance evaluation of fitted models is based on the principle of
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reduction in some statistical criteria such as error sum of squares with training data (SSE t), error sum of squares with checking data (SSE c), variance of the residual error (V r), variance of the modeling error (V m), variance of the error transmitted from input to output (V t), and total variance (V tot).
The traditional selection procedure involves the comparison of the selection criteria for all possible subsets of predictor variables. Three alternative selection methods are used when a large number of candidate predictors are available: the forward, backward and stepwise variable selection procedures. The forward selection procedure starts with no input variables in the model so as to allow the addition of one variable at a time. This process is halted when a satisfactory fit is achieved or when all input variables have been added. The backward selection method begins with all predictor variables in the model and applies a process of elimination. As a result, variables are deleted one at a time until an unsatisfactory fit is encountered. Finally, the stepwise procedure combines features of both the forward and the backward selection procedures. While variables can be added one at a time, the procedure also allows the elimination of variables. Though the F-statistics method is generally applied, the decision to halt the process of adding or eliminating variables can be made using any of the selection criteria mentioned above.
The classical selection methods offer the possibility to isolate one reduced model. However, the main drawback associated to these methods lies in their inability to identify alternative candidate subsets of the same size or a model considered being optimal according to the above selection criteria. Hence, the traditional selection procedures could lead to poor results and often to different subsets because of their inability to consider any interaction between sensors. Thus, the basic condition for a successful implementation of a sensor fusion method requires a simultaneous application of the selection criteria so as to ensure the independent selection of sensors.
The proposed sensor fusion method involves the use of experimental data relative to the individual error components with respect to different factors such as process-input parameters and operating conditions. Accordingly, the use of an efficient test strategy appears to be most appealing. Though not recommended for a prohibitively large number of predictor variables, the factorial design allows the greatest scrutiny of alternative candidates. The use of orthogonal arrays (OAs) would reduce significantly the number of fitted models. The OA-based model building procedure can be summarized in the following steps:
q Collect the training and checking data. The most important factors believed to influence the investigated features must be identified and considered in the measurement tests.
q Select the modeling technique and optimize the training performance.
q Select the OA for the design of models including all potential predictor variables. Every column in the OA corresponds to a variable, V i, with two
ANN Based Sensor Fusion Strategy for Multi-Axis Machine Accuracy Monitoring and Control 9 levels indicating if a sensor information is input to the fitted model (1: included) or not (0: not included).
q Train the models generated in the OA and compute their performance index (SSE t, SSE t, V r, V m, V t and V tot).
q Determine the effect of each variable on every performance index. These effects can be considered as the rate of reduction in the sum squared errors SSE i and variances V i when a sensor is or is not input to the fitted model. For each criterion, the effect of each predictor variable can be estimated by taking the arithmetic difference between the two average values corresponding to the two variable levels (0 and 1). Based on these results, sensors contributing to the reduction of the criteria values are selected.
q Obtain the final fusion model. The final fusion model is built once the sensors providing the best information on the error sources behavior are determined. 3. Models building and simulation
To build the compensation ANN models, the error components were classified into five groups having similar characteristics and requiring the same measurement procedures and instrumentation. These groups are the co-ordinate system thermal drift, the geometric errors, the cutting force induced errors, the inertial errors and the spindle thermal drift.
3.1. Measurement of error components
Using a 5528A-laser interferometer system, measurement of the individual error components was conducted along lines parallel to the machine tool axes. The displacement intervals for recording the error values amounted to 10 mm along the X-axis and 20 mm along the Z-axis. In addition, the transitional drifts and the angular inclination of the spindle axis were also measured using two capacitance sensors.
The behavior of the average thermal drift error of the coordinate system at the zero of the machine tool observed over the machine warm-up is illustrated in Figure 5. It can be seen that the thermal expansion of the machine frame resulting from a 12 hrs run introduced an error of about 25 μm. As shown in Figure 6, the linear displacement error along the X-axis is 15 μm at a nominal position Xn=380 mm. The linear displacement error along the Z-axis reaches at a nominal position Zn=840 mm a maximum value of 70 μm, exceeding the specified machine tool accuracy (± 20 μm). Evaluated at a given machine thermal state, the maximum straightness error reaches 10 μm along the X-axis and 12 μm along the Z-axis. Furthermore, the yaw errors measured along the X and Z axes are similarly presented in Figure 6 and 7. Finally, the behaviour of spindle thermal drift error observed under various running conditions is also illustrated in Figure 8.
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Time [min]
C o o r d i n a t e s y s t e m t h e r m a l d r i f t [m i c r o n ]
720
54036018000
1020
30
Figure 5. Coordinate system thermal drift error
Nominal X position [mm]
X _a x i s d i s p l a c e m e n t e r r o r
340
270
180
90
10
50-5-10-15
Figure 6. The X displacement error measurement (20 °C)
Nominal Z position [mm]
760
570
380
190
Z _a x i s d i s p l a c e m e n t e r r o r
10
0-10-20-30-40-50-60-70
Figure 7. The Z displacement error measurement (20 °C)
S p i n d l e t h e r m a l
d r i f t
e r r o r s
Time [min]
1000
750
500
250
121086420-2
Figure 8. Spindle thermal drift error measurement taken under continuous running conditions
ANN Based Sensor Fusion Strategy for Multi-Axis Machine Accuracy Monitoring and Control 11
3.2. Error modelling
To provide quasi-real time tool position correction, the static, thermal and dynamic errors are predicted using data from an array of sensors. These sensors constantly monitor the machine tool parameters related to the cutting tool nominal position,the temperature at various locations, the forces along three perpendicular directions, the speed, and the feed-rate. Data from the sensing devices is fed into the ANNs in order to estimate the individual error components.
The axial thermal drift error of the spindle δb z
is considered to illustrate the procedure designed to build the ANN based error model. Initial investigation
suggests a strong relationship between δb
z and the spindle speed and the rise of the average temperature along the spindle and the bed location. As a result,measurement data used to train the ANNs was collected at three spindle rotational speeds of 1000, 2000, and 3000 rpm using temperature sensors S1, S2, S3, and S4(Figure1). Before measurement task, the turning center was run at a constant spindle speed for a 12-hrs warm-up period. Then, the axial thermal drift and the temperature history were continuously registered.
Before selecting the variables and training the neural networks, it is important to establish the size of the hidden layer and optimise the training performance. The number of inputs in the models under evaluation is not constant. The idea is to approximate the relationship between the size of the hidden layer and the complexity of each parameter to estimate. For this evaluation 5 nets have been studied {[IxI/2x 1], [IxIx1], [Ix2Ix1], [IxIx2Ix1] and [Ix2Ix3Ix1]}, where I is the number of inputs. The backward error propagation using the generalised delta involves setting the gain and momentum, such that accurate results can be achieved within the shortest time. For all trained models, an adequate knowledge representation with an average error of less than 1% was observed, irrespective of the hidden layer size. Consequently, to avoid long training and overfitting that could disturb its accuracy, the [I x I+1 x 1] network structure was selected.To select the predictor variables, the procedure consists in establishing the OA for the design of models including all potential predictor variables. As illustrated in table 1, the OA that fits in this procedure is the L8 representing a total of 8 models to be designed.
A relatively accurate relationship for δb z
prediction is obtained using [I x I+1 x 1]three layer ANNs models. Results relative to each model for both training and checking operations are also presented in table 1. Deviations of the model's estimates are presented as a function of six selection criteria. All models fitted the training data relatively well as indicated by the SSE t and the V r . As shown in table 1, the results obtained from the checking data were less accurate.
Using these results, the effect of each predictor variable on the selection criteria was evaluated. As illustrated in Figure 9, the average effect of each input variable on the six criteria is represented by its contribution to each model accuracy
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improvement. These results reveal that only the temperature sensors S 2 and S 4 and the spindle speed N have positive effects on the models. Curve fitting results of the spindle axial thermal drift error obtained from training the ANN model with the selected subset are shown in Figure 10. Examination of these results demonstrates that the ANN model can fit the thermal drift very well. Generally, the sensor fusion procedure reveals that each individual error component depends strongly on the temperature sensors distributed along its generative axis.Table 1. Subsets variables evaluation
Predictor variables
Selection criteria Model #
N S1S2S3S4SSE t SSE c
V r V m V t V tot 1111118.9217.920.3350.0720.1060.84621110012.9833.380.4020.1240.139 1.11831001110.3420.170.3580.090.0940.89441000089.34256.31.047 1.1080.198 3.60350101022.2744.330.5240.2440.245 1.53460100110.8322.490.3660.0960.1050.93870011026.2153.510.5690.2950.174 1.618
010128.23
44.02
0.59
0.322
0.134
1.565
Selection c riteria
P r e d i c t o r v a r i a b l e c o
n t r i b u t i o n %
SSEt 0
2040
60
SSEc Vr Vm
Vt Vtot
Figure 9. Average contribution of the predictor variables in reduction of statistical criteria
Time [min]
A x i a l s p i n d l e e r r o r [m i c r o n ]
1000
750
500
250
2520151050-5
Figure 10. The measured and the modelled spindle axial thermal drift as the machine warm-up.
ANN Based Sensor Fusion Strategy for Multi-Axis Machine Accuracy Monitoring and Control 13
The sensor fusion procedure described above and used to model the spindle axial thermal drift error has also been similarly implemented to establish specific optimal models for the rest of the individual errors. The results of these ANN sensor fusion models are summarized in Table 2. The line entries correspond to the monitored machine parameters or variables. The column entries are the measured individual errors associated to various error sources. For each error, the sign "x" is used to identify the optimal combination of the monitored parameters. Obviously, all of the variables could have been used as inputs to each model. Consequently, the modelling procedure could have been simplified to the detriment of the error prediction accuracy.
Table 2. Selected optimal subsets variables for models building
Geometric errors Thermal drift
X axis
Z axis
Spindle errors
Load induced errors
Inertial
errors
V A R
δd
x δd
z
δx x
δx z εx y δz z δz x
εz y δsp x δsp z
εp y
δf
x δf
z
δi
x δi
z
Px x
x
x Position
Pz x x
x
S1x x x x x x S2x x x
x x S3x
x x
x x x x x
S4x
x x
x
x x x
x
S5x x
x x S6x x
x
S7x x
x
x x S8x
x x
x
Temp.
sensors
Sm x x x
x
Fx Fy x x Force
Fz x
x
Vx x
Vy x
Speed
N
x
x
x
Indeed, the accuracy of the models presented in Table 2 has been thoroughly investigated. The results of this evaluation are shown in Figures 11-13. It can be seen that the maximum residual error that is the difference between the observed quantity and the response of the model is less than 1μm. Compared to the maximum linear displacement error observed along the X-axis (15μm) and the Z-axis (70μm), it can be concluded that the proposed sensor fusion technique offers the ability to predict the individual errors with sufficient accuracy.
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#1#2#3
#4#5
#7#8
R e s i d u a l e r r o r s m i c r o n s / A r c s e c
0.125Measuring cycle #
Figure 11. Maximum residual prediction errors of geometrical deviations under
various thermal conditions
1000
0.20.40.30.1
20003000Spindle speed (Rpm)
R e s i d u a l e r r o r s m i c r o n s / A r c s e c Figure 13.
Maximum residual prediction errors of spindle deviations
0.501.001.250.750.25
R e s i d u a l e r r o r s m i c r o n s / A r c s e c
Measuring cycle #
Figure 12. Maximum residual prediction errors of load-induced and inertial errors
under various thermal conditions
ANN Based Sensor Fusion Strategy for Multi-Axis Machine Accuracy Monitoring and Control 15
3.3. Simulation
Once the various individual error models were established, the compensation values ?x and ?z are synthesized using the algorithm illustrated in Figure 4. Simulation tests conducted using various machine tool conditions display the effectiveness of the proposed compensation approach.
Figures 14-16 visualize the spatial-variant error components at an arbitrary temperature (average temperature of 24.3 °C) and show a comparison of measured and predicted errors in the X-Z plane. Maximum errors without compensation are 30 μm in the X-direction and 65 μm in the Z-direction. As illustrated in Figure 16, residual errors estimated after compensation are within a 2 μm range. The aptitude of the model to identify thermal effects has also been verified. The models were tested under different machine thermal conditions. Thermal effects were predicted with an average error of less than 2 μm. The approach provided good prediction capabilities, particularly when the machine tool average temperature varies within a range of 18 °C.
Figure 14. Measured error surfaces in X and Z directions
Figure 15. Predicted error surfaces in X and Z directions
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Figure 16. Residual error surfaces in X and Z directions
4. Conclusion
In response to the increasing demand for higher quality of machined parts, accuracy improvement of multi-axis CNC machines through software error compensation has become increasingly important in modern manufacturing. The success of such approach depends on the degree of accuracy, robustness and reliability of the model to estimate on-line the resultant error components at any location within the machine workspace. The neural network-based multi-sensor fusion approach proposed in this paper is built to satisfy these requirements. Applicable to multi-axis machines of divers configuration, the proposed general methodology has been developed to formulate the mathematical model that relates quasi-static and dynamic errors to the machine tool operating conditions. Combined to the selection of optimal subset sensors, the use of the ANN technique provides a powerful modelling tool of the individual error components. The procedure expresses in a rational clear manner the contribution of the machine conditions to the individual error components. Indeed, the performance evaluation of the error compensation approach led to a significant reduction of the machine error to an average error of 2 microns.
In addition to its applications for improving point-to-point positioning accuracy, the methodology reported herein can help designers and users evaluate machine tools performance for acceptance, testing and identifying the machine's optimal working region.
5. Acknowledgements
The authors are grateful to the Natural Sciences and Engineering Research Council of Canada for their financial support through individual grant RGPIN# 217395. 6. References
[BEL 87]Belfore, G., Bona, B., Canuto, E., Donati, F., Feraris, F., Gorini, I., Morei, S., Peisino, M., and Sartori, S., "Coordinate Measuring Machine And Machine Tool
Selfcalibration", Annals of the CIRP Vol. 36, No. 1, 1987.
ANN Based Sensor Fusion Strategy for Multi-Axis Machine Accuracy Monitoring and Control 17 [BRY 82]Bryan, J. B., "A Simple Method for Testing Measuring Machines and Machine Tools, Part I", Precision Engineering, Vol. 4, No. 2, 1982.
[BRY 82]Bryan, J. B., "A Simple Method for Testing Measuring Machines and Machine Tools, Part II", Precision Engineering, Vol. 4, No. 3, 1982.
[DON 86]Donmez, M. A., Blomquist, D. S., Hoccken, R. J., Liu, C. R. and Barash, M. M., "A General Methodology for Machine Tool Accuracy Enhancement by Error
Compensation", Precision Engineering, Vol. 8, No. 4, 1986.
[FER 86]Ferreira, P. M. and Liu, C. R., "A Contribution to the Analysis and Compensation of the Geometric Error of Machining Center ", Annals of the
CIRP, Vol. 35, No. 1, 1986.
[HOC 77]Hocken, R. J., Simpson, J. A., Borchardt, B., Lazar, J., Reeve, C. and Stein, P., "Three Dimensional Metrology" Annals of the CIRP, Vol. 26, No. 2, 1977.
[LOV 73]Love, W. J. and Scarr, A. J., "The Determination of the Volumetric Accuracy of Multi-Axis Machines", MTDR Proc. Conf., Vol. 14, 1973.
[MCC 88]McClelland L. and Rumelhart D. E., "Explorations in Parallel Distributed Processing", MIT Press, 1988.
[POR 80]Portman, V. T., "Error Summation in the Analytical Calculation of Lathe Accuracy", Machines & Tooling, Vol. 51, No. 1, 1980.
[RAG 85]Ragunath, V., "Thermal Effects on the Accuracy of Numerically controlled Machine Tools" Ph. D dissertation, Perdue University, West La fayette, Indiana,
1985.
[SCH 77]Schultschick, R., "The components of volumetric Accuracy", Annals of the CIRP, Vol. 25, No. 1, 1977.
[ZHA 85]Zhang, G., Veale, R., Charlton, T., Borchardt, B. and Hocken, R., "Error Compensation Of Co-ordinate Measuring Machines", Annals of the CIRP Vol.
34, No. 1, 1985.
五款最好的免费电脑资料同步备份软件
文件夹同步就是将两个文件夹内的文件内容进行分析，可选择性的让两个文件夹内容保存一直。文件夹同步软件相当有用，虽然大多数人没用过，但它确实能够为你节省很多时间和操作。比如说：同步U盘上的数据和软件设置，查找软件版本区别和更新，同步FTP上的数据。我认为，很多情况下使用同步软件可以极大提高计算机操作效率。 高效文件同步工具GoodSync 在多种驱动设备之间自动同步和备份，（个人电脑、移动设备、网络设备）支持任何文件类型，支持多任务、多语言。人性化的界面，可自由选择部分单向双向同步，有强大的过滤系统，有完整的日志记录及更改内容报表。 注意：GoodSync分析之后会在任务文件夹生成“_gsdata_”的隐藏文件夹，里面存放在任务日志和备份文件。GoodSync有免费版和专业版之分。免费版在30天内没有任何限制，仅仅是不能可用于商业用途和政府机构。过来三十天依然可以免费使用，但是仅支持3个任务（相比很多单任务的还是强大不少）和每次100文件夹的同步工作（一般情况下够）。下载 开源同步软件FreeFileSync 界面简洁，操作简单。虽然是单任务，但是可以保存和加载配置。最重要的是，作为一款开源如软件，它没有任何限制。下载
多文件夹同步器Allway Sync Allway Sync 是一个非常容易使用的 Windows 文件同步软件。同样支持在多种设备进行同步、多向同步（1个文件夹到N个）、自动同步。有极其强大的过滤规则、错误管理，可以压缩备份、加密备份。可导出导入xml格式配置文件和任务。免费版有文件大小和数量限制。当然，有着强大功能的同时，体积和资源占用也偏大。下载
(完整版)博客系统需求分析
校园博客系统需求分析 评审日期：2010 年04 月01 日 目录 1导言 (1)
1.2范围 (1) 1.3缩写说明 (1) 1.4术语定义 (1) 1.5引用标准 (1) 1.6参考资料 (2) 2系统定义 (2) 2.1项目来源及背景 (2) 2.2系统整体结构 (2) 3应用环境 (3) 3.1系统运行网络环境 (3) 3.2系统运行硬件环境 (4) 3.3系统运行软件环境 (4) 4功能规格 (4) 4.1角色（ A CTOR ）定义 (5) 4.1.1博客访问者 (5) 4.1.2管理用户 (5) 4.1.3 数据库 (6) 4.2系统主U SE C ASE图. (6) 4.3客户端子系统 (6) 4.4管理端子系统 (8) 4.4.1 登录管理 ....................................................... 10 4.4.2 类型管理 ......................................................... 11 4.4.3 评论管理 ....................................................... 12 4.4.4 留言管理 ....................................................... 12 4.4.5 图片管理 ....................................................... 12 4.4.6 用户管理 ....................................................... 13 5性能需求 (13) 5.1 界面需求 (13) 5.2响应时间需求 (13) 5.3可靠性需求 (13) 5.4开放性需求 (14) 5.5可扩展性需求 (14) 5.6系统安全性需求 (14) 6产品提交 (14)
个人博客简介
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汇博通文档借阅管理组织系统软件使用使用说明
汇博通文档借阅管理系统使用说明书 汇博通知识管理系统的属性管理，实际上已提供了借阅与归还功能，但那是针对每一份文件 或档案而言的。 这里，为客户提供一款专门用于文档的借阅与归还的软件，不但可办理一份文件的借阅或归 还手续，只要有需要，也可批量办理借阅与归还，另外，还提供了与借阅有关的一系列统计 报表。 发放功能与借阅类似，所不同的只是发放不必归还，如将购买的资料、图书发放给职员学习 等。 注：借阅与归还模块的操作，需要获得以下三种权限中的一种： 系统管理员 归档授权（档案管理员） 编号授权（文件管理员） 与借阅与归还模块相关的系统参数的设置说明如下：
首页 汇博通主页的模块工具条上，有一个借阅与归还的按钮，单击它即进入借阅与归还首页。 借阅（发放） 前面已经介绍过，借阅与发放的区别在于，借阅需要归还，发放则不必归还，从某种意义上 来说，发放实际上已将所有权（或有条件的所有权）转移给接收者。 借阅界面包括左右两个子窗体，左侧子窗体用于显示可供借阅（发放）的文档，其上部有搜 索关键词输入框，输入相应关键词即可查找出可供借阅的相应文档，如果要借阅的文档已经 在操作者手上，并且，标注有条形码或电子标签，操作者可直接通过条码阅读器或电子标签
阅读器读取相应编码直接获取到该文档。 根据实际需要，通过点选左侧的复选框，选择具体文档，然后，通过点击两个子窗体中间的箭头，即可将选中的文档添加到右侧子窗体的列表中，即可直接办理借阅或发放手续。 可供借阅（发放）检索列表待选区。借阅（发放）选择勾选列表区。 可供借阅（发放） 输入文件名称、编号、责任者或主题词等属性，点击【检索】按钮进行查找，如下图： 勾选确定后点击该按钮，即可添加到已 选择列表区中。
如何使用群晖备份、同步文件
如何使用群晖备份、同步文件？通过群晖管家安装好NAS之后，想要实现备份、同步还要随时随地查看所有的文件？只需要一个Drive，就能把你的需求统统搞定。让你轻松的掌握文件同步和备份。 Drive既是备份盘、同步盘、网盘，还可以是协作盘。集中管理所有文件，还能够同步不同电脑上的数据。团队脑风暴时，可以多人在同一个文档上实时协同编辑，还能够备份电脑上的文件并且提供多版本保护。 安装及设置drive套件 1、打开群晖DSM界面，在套件中心安装Drive套件。 2、安装Drive套件会一并安装Drive管理控制台——顾名思义，就是可以设置Drive 相关功能、管理所有备份和同步的设备、查看历史版本等。 3、建议你在Drive管理控制台启用深度搜索，就可以在Drive里面查找内文关键字还有照片各种原始信息，步骤如图。
设置备份盘 1、首先进入群晖官网的下载中心，根据NAS机型，选择下载“Drive Client”PC客户端，Windows、Mac、Ubuntu一应俱全，系统兼容妥妥的。 2、PC客户端安装完成后，根据需求修改Drive服务器（NAS端）和电脑（本地端）的不同文件夹。 3、设置完成后，进入Drive PC客户端的控制面板，将同步模式改成“单向上传”，点击应用，然后就开始备份啦。 同步盘如何实现 1、同步盘很简单，设置步骤跟上面的备份盘一样，在需要同步的电脑上安装Drive PC客户端，并且选择双向同步。 2、如果在办公场景，希望把他人分享给你的共享文件夹同步到电脑本地，在PC客户 端控制面板点击“创建>启用同步与我共享”，这么一来，别人与你分享的文件也会同步到本地。
日志分析系统
Web日志集中管理系统的研究与实现 吴海燕朱靖君程志锐戚丽 （清华大学计算机与信息管理中心，北京100084） E-mail：wuhy@https://www.wendangku.net/doc/4a11166881.html, 摘要： Web服务是目前互联网的第一大网络服务，Web日志的分析对站点的安全管理与运行维护非常重要。在实际运行中，由于应用部署的分散性和负载均衡策略的使用，使得Web日志被分散在多台服务器上，给日志的管理和分析带来不便。本文设计并实现了一个Web日志集中管理系统（命名为ThuLog），系统包括日志集中、日志存储和日志分析三个模块。目前，该系统已经在清华大学的多个关键Web应用系统上进行了应用，能够帮助系统管理员清晰地了解系统运行情况，取得了较好的运行效果。 关键词：Web日志日志分析日志集中管理系统 The Research and Implementation of a Centralized Web Log Management System Wu Haiyan Zhu Jingjun Cheng Zhirui Qi Li (Computer&Information Center,Tsinghua University,Beijing100084) Abstract：Web is now the biggest network service on the Internet.The analysis of Web logs plays an important role in the security management and the maintenance of a website.But because of the decentralization of deployment and the use of load balancing,Web logs are often seperated on each Web server,which makes the management and analysis of them not so convenient.This paper designs and implements a Web Log Centralized Management System(named ThuLog),which includes3modules:the centralization of logs,the storage of logs and the analysis of logs.Through log analysis of several critical Web systems in Tsinghua University,it could help system administrators learn clearly what happens in information systems and achieves good operating results. Key words：Web Logs Log Analysis Web Log Centralized Management System 1.引言 近年来，随着计算机网络技术的迅速发展，Web正以其广泛性、交互性、快
博客系统需求分析报告
博 客 系 统 需 求 分 析 报 告 院系：信息电子工程学院 班级：软件08-1 设计小组人员：29号 日期：2010年5月24日
一、系统概述 “博客”一词是从英文单词Blog音译（不是翻译）而来。Blog是Weblog 的简称，而Weblog则是由Web和Log两个英文单词组合而成。 Weblog就是在网络上发布和阅读的流水记录，通常称为“网络日志”，简称为“网志”。博客（BLOGGER）概念解释为网络出版（Web Publishing）、发表和张贴（Post-这个字当名词用时就是指张贴的文章）文章，是个急速成长的网络活动，现在甚至出现了一个用来指称这种网络出版和发表文章的专有名词——Weblog，或Blog。 在网络上发表Blog的构想始于1998年，但到了2000年才开始真正流行。而2000年博客开始进入中国，并迅速发展，但都业绩平平。直到2004年木子美事件，才让中国民众了解到了博客，并运用博客。2005年，国内各门户网站，如新浪、搜狐，原不看好博客业务，也加入博客阵营，开始进入博客春秋战国时代。起初，Bloggers将其每天浏览网站的心得和意见记录下来，并予以公开，来给其他人参考和遵循。但随着Blogging快速扩张，它的目的与最初已相去甚远。目前网络上数以千计的Bloggers发表和张贴Blog的目的有很大的差异。不过，由于沟通方式比电子邮件、讨论群组更简单和容易，Blog已成为家庭、公司、部门和团队之间越来越盛行的沟通工具，因为它也逐渐被应用在企业内部网络（Intranet）。目前，国内优秀的中文博客网有：新浪博客，搜狐博客，中国博客网，腾讯博客，博客中国等。 二、需求分析 博客系统是一个多用户、多界面的系统，主要包括以下几个模块组成。 1.匿名用户模块 本模块主要由注册、登录、浏览博客、评论4个部分组成。匿名用户可以对其他用户的博客内容时行浏览、评论。也可以通过注册后登录博客系统，申请一个属于自己的博客。 2.注册用户模块 本模块主要由个人信息管理、评论管理、好友管理、相册管理、文章管理5
人力资源管理系统软件操作手册
XX集团—人力资源管理系统操作手册 目录 常用操作（新人必读） (2) 1.基础数据管理 ................................................................................................................... - 5 - 1.1组织架构 (5) 1.2职位体系 (8) 1.3职员维护 (11) 1.4结束初始化.................................................................................. 错误！未定义书签。 2.组织管理业务 ................................................................................................................. - 27 - 2.1组织规划 (27) 2.2人力规划 (33) 2.3组织报表 (38) 3.员工管理业务 ................................................................................................................. - 41 - 3.1员工状态管理 (41) 3.2合同管理 (41) 3.3后备人才管理 .............................................................................. 错误！未定义书签。 3.4人事事务 (52) 3.5人事报表 (59) 4.薪酬管理 ......................................................................................................................... - 69 - 4.1基础数据准备 (69) 4.2薪酬管理日常业务 (92) 4.3薪酬管理期末业务 (107) 4.4薪酬报表 (108)
销售管理软件操作手册
前言 本《操作手册》内容是按该软件主界面上第一横排从左至右的顺序对各个功能加以介绍的，建议初学者先对第一章系统设置作初步了解，从第二章基础资料读起，回头再读第一章。该管理软件的重点与难点是第二章，望读者详读。 第一章系统设置 打开此管理软件，在主界面上的左上方第一栏就是【系统设置】，如下图所示： 点击【系统设置】，在系统设置下方会显示【系统设置】的内容，包括操作员管理、数据初始化、修改我的登录密码、切换用户、选项设置、单据报表设置、导入数据、数据库备份、数据库恢复、压缩和修复数据库、退出程序。下面分别将这些功能作简要介绍： 1.1操作员管理 新建、删除使用本软件的操作员，授权他们可以使用哪些功能。此功能只有系统管理员可以使用。 1.1.1 进入界面 单击【系统设置】，选择其中的【操作员管理】，画面如下：
1.1.2、增加操作员 单击【新建】按钮，画面如下： 输入用户名称、初始密码、选择用户权限，可对用户进行适当描述，按【保存】后就点【退出】，就完成了新操作员的添加，效果如下图。
1.1.3 删除操作员 选择要删除的操作员，单击【删除】按钮。 1.1.4 修改操作员 选择要修改的操作员，单击【修改】按钮，可对操作员作相应修改，修改后需保存。 1.1.5 用户操作权限 选择要修改的操作员，单击【修改】按钮，出现以下画面，点击【用户权限】栏下的编辑框，出现对号后点【保存】，该操作员就有了此权限。 1.2数据初始化 1.2.1进入界面 单击【系统设置】，选择其中的【数据初始化】，画面如下：
1.2.2数据清除 选择要清除的数据，即数据前出现对号，按【确定】后点【退出】，就可清除相应数据。 1.3 修改我的登录密码 1.3.1进入界面 单击【系统设置】，选择其中的【修改我的登录密码】，画面如下： 1.3.2密码修改 输入原密码、现密码，然后对新密码进行验证，按【确定】后关闭此窗口，就可完成密码修改。 1.4 切换用户 1.4.1进入界面 单击【系统设置】，选择其中的【切换用户】，画面如下：
多文件夹的自动同步和各向同步工具
多文件夹的自动同步和各向同步工具 出处：小建の软件园作者：佚名日期：2008-06-25 关键字：同步 对于经常需要备份文件，同步文件的网友，Allway Sync 可谓不可多得，虽然不能激活其专业版，对文件数量多和经常性的同步操作可能会超过免费版的限制，不过对于一般文件数量不多同步操作可以完全满足，Allway Sync 使用相当简单，多种同步方式能满足你不同需求。对重要文件进行备份是文件恢复最好的方法，而 Allway Sync 可以简化你许多备份的过程，能实现自动备份，如果你“胃口”不大，免费版应当已经可以满足。 下载地址：https://www.wendangku.net/doc/4a11166881.html,/soft/23495.html Allway Sync 可以进行自动同步，可以对的文件/文件夹进行筛选，只备份需要的东东。
Allway Sync 备份方式介绍 - 同步方式有源文件夹同步和各向同步两种方式： 1、源文件夹同步方式将以一个文件夹为基准，删除或覆盖其余文件夹与源文件相比较不相同的文件。 2、各向同步方式则自动将更新的文件覆盖几个同步文件夹中的旧文件。软件带有一个小型数据库，监视每次更新后的文件状态。如果在一次同步之后，你删除了同步文件夹中某些文件，它在同步的时候将其它的几个文件夹的副本也删除，而不会将不需要的未删除文件重复拷贝到已更新的文件夹。由于软件自己会对文件进行删除和覆盖，它提供了使用回收站进行文件备份的措施，使用者可以在不慎执行错误的同步动作之后，从回收站将错误删除或覆盖的文件找回来（默认禁用该功能，请到软件选项处激活相应设置）。 主程序在 AllwaySync＼Bin＼里面，此为多国语言版，在语音选项那里选择中文即可。不过退出的时候会有错误提示（貌似没影响？）
博客系统需求分析报告1
系统需求分析和概要设计 1 系统需求分析 1.1 开发背景 过去很多人都喜欢写文章写日记以及交流自己的文章和作品，以求实现相互间的沟通、展现自己的才华和让别人了解自己的想法观点。现在的网络已经成为人们生活中不可或缺的一个元素，所以自然而然诞生了博客这样一个新兴事物，它不仅仅能取代前面所说的功能，还能加入图片，而且使得作者更能无所拘束地生动地写出自己想写的，旁人也能非常便捷地阅读并且加以评论，并且它还能作为展示个人个性的窗户。个人博客现在已经成为很多人生活中必不可少的一个部分，方便了人与人之间的沟通和交流。 1.2 系统实现目标概述 基于个人博客以上的特点，本系统要实现个人博客的主要基本功能有主界面，博客用户登录发表文章（心情、日志），用户登录/退出，游客发表评论，分页浏览文章和评论等。这里其中比较主要的是区分了个人博客用户和游客。博客用户可以在任何时候写下自己的主张，记录下自己的点点滴滴。而游客主要的权限是阅读博客所有注册用户写的文章，阅读后可以发表评论和留言，还可以分页浏览所有注册用户上传的图片。以上是个人博客的系统功能目标，当然由于个人博客的网络流行特点以及个人个性的展示，还适当要求界面比较漂亮轻快，直观便捷，操作方式简单以及人性化。 1.3 系统功能需求 根据对系统的特点和应用的分析，可以得到本系统主要有如下功能： （1）登录 这部分功能又分为用户登录、用户退出两个部分。 登录：主要用于验证博客网站用户信息的真实身份，以便对博客网站进行管
理和维护。通过系统管理员写入的用户名，密码登录到网站。网站检测用户的用户名，密码并给予其相应的权限对博客网站进行操作。 用户退出：已经登陆的用户可以退出，释放自己所占有的各种信息资源。 （2）文章管理 文章管理主要有文章的发表、查询、浏览、评论和删除功能。 博客的系统管理员除了可以查询、浏览和评论文章外，还可以对系统中的所有文章以及评论进行修改、删除操作。这些维护和管理拥有最高权限，并且系统自动更新在服务器端数据库中的数据。 文章的发表：博客用户可以发表自己的文章，文章包括主题、正文、表情、图片等信息，作者通过各种元素来展示自己的想法和思想。系统接受这些信息并且存储在服务器端的数据库中。 文章的删除：博客用户可以删除自己已经发表的文章内容和各项信息，系统自动在服务器端数据库中删除这些记录。 文章的浏览：游客和博客用户根据所获得的用户权限获取服务器端数据存储的各篇文章并且浏览阅读文章的所有信息，包括标题、正文、表情、图片以及其它读者的留言评论。 文章的评论：文章的读者可以评论和回复所阅读的文章，发表自己的看法。系统自动将这些评论存储在服务器端的数据库中，并且可供博客作者以及其它读者浏览。 文章的查询：博客用户可以按文章题目或作者来查询想要查的文章。 文章中还可能包含一些图片视频等多媒体,所以文章管理中还包含了网站中媒体的管理。 媒体管理有添加，浏览、删除和查询功能。博客用户可以添加自己喜欢的图片或视频等，还可以查询和浏览系统中的所有媒体信息。游客只能浏览博客系统中的媒体信息。系统管理员拥有以上的所有权限，除此之外还可以删除媒体信息。 （3）博客管理员管理 博客管理员可以添加、删除新用户，用户的角色又分为订阅者、作者、编辑、投稿者、管理员。 还可以对博客主页的外观、博客使用的插件、工具进行添加、删除、设置。
博客作用
1.过滤信息 在这个网络信息泛滥的时代，网上的信息太多、太杂、太乱，学习者无法判别哪些信息是有价值的，哪些是重要的。教师可以通过博客将经过过滤过的信息传递给学生，而学生也可以通过博客将信息传递给他的伙伴。通过浏览别人的博客日志，知识获取的效率将得到很大的提高。 2.提供学习的丰富情境 通常的教辅网站，只是提供一些参考资料的链接，而博客则提供更多的评价，更广泛的背景资料。有一些学者通过博客日志反映他们对某些问题的认识，开始对于这些问题的看法可能也是粗糙的，但是他们将这些思想表达出来，然后在博客上发表后续的看法。在这一过程中，专家可以将最近看了哪些书，读了哪些人的文章，听取了哪些意见都通过博客方式表达出来。这样，阅读者了解的不仅仅是专家静态的、目前的观点，而重要的是可以把握专家思想的流程。同样，这一方式对于学生来讲也是有效的，学生的博客日志可以反映出他们在学习过程中产生的问题、关于问题的想法与思路、问题的解决过程，使得教师可以更有效地了解学生的学习状况。 3.提高学生的媒体文化水平 博客（blog）的个人化使得博客们（blogger）在信息发布的过程中，要采用最适当的方式对信息进行过滤与说明，使得他的博客日志能够为更多的人接受，使得他的思想和资源为更多的人所了解。与传统BBS相比，博客日志具有更强的规范性，博客们具有更强的自律性。由于博客一般是由个人或小组拥有的，通常具有共同的主题，所谓敝帚自珍，所以在博客的世界中，很少出现在BBS中常见的不负责任的"胡说八道"。 4.鼓励参与者发表自己不同的观点 博客的模式是平等的，博客更看重的是参与的过程而不是结果。对于教师或书本上的观点，学生可以通过博客的方式发表他对于这些问题的理解，博客并不要求意见的统一，但要求意见的针对性和独立性。另外，在课程设置的过程中可以设置多个不同的议题，允许学生自由地选择他们感兴趣的议题。 5.提供对信息的评价 博客的重要特征就是对信息的过滤，使得信息可以转换成有用的知识。但是
备份软件使用方法v1.0
备份软件使用方法 一Bestsync2012使用说明 1 软件运行 点击BestSync2012运行软件 2 设置任务 在编辑菜单下点击追加任务（如果任务列表下没有任务可以在文件菜单下选择新建任务选项） 软件会弹出任务窗口，用来设置同步任务
以其中一个任务为例
选择好同步的文件夹和同步方向，点击下一步，按照要求设置任务即可。 3 查看任务 在以有任务中点击设置任务（任务必须是未在同步状态，否者不能点击设置任务选项）
点击后软件会弹出设置同步任务窗口，在这里可以在里面进行任务修改和设置
目前我们设置的同步任务只需要修改一般和日程两个窗口下的内容，其他暂时不需要修改。 BestSync2012这款同步软件目前还不是很稳定，需要不定期检查一下软件是否运行正常，如果发现软件出错，就关闭软件后在打开BestSync2012软件，因为打开软件后软件不会自动启动同步功能，所有需要手动启动所有任务 注意： 1 在修改任务在开启后，必须将修改的任务停止一下在开启，不然同步任务不能正常同步。 2 现有BestSync2012同步软件在16.15和151.247这两台机器上。
二Backup Exec 2010 R2 SP1使用说明 1 软件运行 点击Backup Exec 2010运行软件 2 设置任务 在作业设置选项中可以看到作业的作业名称、策略名称和备份选这项列表。 其中作业名称里放有现有作业，双击其中一个作业就可以看到作业属性。作业属性默认显示设备和介质窗口，在设备和介质窗口下可以选择设备和介质集。目前设备选项中因为只有一台磁带机工作，所有只有一个选项，而介质集一般选择永久保留数据-不允许覆盖选项。
博客需求分析
博客系统需求分
一、系统概述 “博客”一词是从英文单词Blog音译（不是翻译）而来。Blog是Weblog 的简称，而Weblog则是由Web和Log两个英文单词组合而成。 Weblog就是在网络上发布和阅读的流水记录，通常称为“网络日志”，简称为“网志”。博客（BLOGGER）概念解释为网络出版（Web Publishing）、发表和张贴（Post-这个字当名词用时就是指张贴的文章）文章，是个急速成长的网络活动，现在甚至出现了一个用来指称这种网络出版和发表文章的专有名词——Weblog，或Blog。 在网络上发表Blog的构想始于1998年，但到了2000年才开始真正流行。而2000年博客开始进入中国，并迅速发展，但都业绩平平。直到2004年木子美事件，才让中国民众了解到了博客，并运用博客。2005年，国内各门户网站，如新浪、搜狐，原不看好博客业务，也加入博客阵营，开始进入博客春秋战国时代。起初，Bloggers将其每天浏览网站的心得和意见记录下来，并予以公开，来给其他人参考和遵循。但随着Blogging快速扩张，它的目的与最初已相去甚远。目前网络上数以千计的Bloggers发表和张贴Blog的目的有很大的差异。不过，由于沟通方式比电子邮件、讨论群组更简单和容易，Blog已成为家庭、公司、部门和团队之间越来越盛行的沟通工具，因为它也逐渐被应用在企业内部网络（Intranet）。目前，国内优秀的中文博客网有：新浪博客，搜狐博客，中国博客网，腾讯博客，博客中国等。 二、需求分析 博客系统是一个多用户、多界面的系统，主要包括以下几个模块组成。 1.匿名用户模块
博客简介
漫漫教学路，博客伴我行能在互联网上拥有一个真正属于自己的空间，是我的梦想，而 今天这个梦想在“博客”中实现了。我怀着一颗好奇心，在博客上流连，申请了一方属于自己的免费空间，置身于梦幻秋天的背景下，设置自己喜欢的几个栏目，于是我便拥有了博客。当我第一次在博客中添加文章的时候，兴奋得无法入眠。我想：平素与网络无缘的我也终于拥有了一个网上家园。一个可以让我任意挥洒激情、记录人生轨迹的网上家园。感谢博客给我一块自由的空间，让我展翅飞翔！在与博客“亲密接触”的日子里，我深深地感觉到博客对教育的促进，对自身专业化成长的帮助。 在开始的时候，我也只是摘录一些自己感兴趣的信息，很少有经过自己思考的原创日志。随着对博客认识的加深，以及浏览其他著名博客所受到的启示，我也试着把自己在教育中的思考及时记录下来。就这样我在博客里“书写着，记录着，思考着，分享着，品味着，学习着”，在不断地积累中感受着学习的乐趣。在博客里写作已经逐渐成为我的一种习惯，在博客中我不断地阅读、书写，在阅读、书写中释放心情，这让我感到在博客中学习竟是如此快乐。我在博客中开设了心灵随笔，教学案例，教学反思，教学相长，教学设计，教学论文，主题中队会等栏目…… 作为一名教师，我深深地认识到：要想鼓励、指导学生写出好的文章，教师必须要有过硬的写作基本功，博客中的心灵随笔这个栏目正好为我提供了这样一个平台，我在这个栏目中及时捕捉教学生活中细微的瞬间，从中悟出深刻的道理，并马上形成文字。例如：《让心灵跟着爱飞翔》、《如何赏识学生》、《感动》、《怎样转变学生的不良习惯》等文章。心灵的感悟，出乎意料的发挥了作用，有了这样的历练，对学生进行写作指导和评改，就驾轻就熟了。学生也会在老师的指导下逐渐明白，写作并不是一件很难的事情，只要真实的记录自己在生活中的所见、所闻，有了自己的感悟，慢慢就会写出具有真情实感的文章。 在博客中记录教学过程是一个不断充实自己，提高自己的过程，教学中我也曾遇到过很多困惑，于是把这些困惑书写到我的博客中，期待与博友们交流和切磋。博友们的热心触发了我很多灵感，常常使我茅塞顿开……我现在博客中的教学案例就是平时点滴的积累。《位置与方向》《那只松鼠》《笔算除法》《商中间、末
管理软件使用说明书
目录 1 软件介绍...................................................... １ 2 软件运行环境 ................................................. １ 3 软件安装步骤 ................................................. １ 4 软件卸载步骤 ................................................. ４ 5 软件使用...................................................... ４5.1、创建数据库.............................................................................................................................. ４ 5.2、创建数据数据表................................................................................................................... ６ 5.3、历史数据读取 ........................................................................................................................ ７ 5.4、查看历史数据、通道信息.............................................................................................. ８ 5.5、打印数据、曲线或图片输出 .................................................................................... １３ 5.6、数据实时采集 .................................................................................................................... １５ 6 软件使用中可能出现的问题与解决方法.................. １８6.1、不出现对话框 .................................................................................................................... １８ 6.2、数据库不能建立............................................................................................................... １８ 6.3、U盘不能数据转存........................................................................................................... １８ 6.4、U盘上没有文件 ................................................................................................................ １８ 6.5、U盘数据不能导入计算机；...................................................................................... １８
GoodSync同步软件完美注册
GoodSync同步软件完美注册、本地同步图文教程 出处：西西整理作者：西西日期：2012-4-12 15:22:15 [大中小] 评论: 0 | 我要发表看法 文件管理这件看似简单的事，真的不简单，因为为了防止意外情况，你需要对文件进行备份，时间一久随着文件数量的增加，再加上有时也会临时队备份文件进行修改等。再想查出这个是最新的、文件有木有全部备份等….就没那么容易了吧！其实这一切说了很简单，因为你可以请：GoodSync软件来帮忙！ GoodSync是一款简单可靠的文件备份和文件同步软件，可以实现两台电脑或者电脑与U盘之间的数据文件的自动同步。GoodSync可以在本地U盘与电脑之间，以及U盘、移动硬盘或电脑与服务器、外部驱动器、W indowsM obile设备、网友、网盘等之间自动同步或单向备份数据。它能自动分析、同步、备份您的电子邮件、珍贵照片、联系人、电影视频、音乐文件、财务文件和其它重要文件。再也不会遗失您的电子邮件，照片，MP3等。 由于GoodSync为共享收费软件，所以这次西西带来的是官网原版+注册机（下载地址，下载的压缩包内含官网下载的GoodSync v9.1.5.5主程序和注册机以及注册说明），还是那句老话：如果你有能力请支持购买正版的GoodSync，如果….就低调吧！好吧！一起来看下注册方法吧！ GoodSync 注册方法： 1、首先下载压缩包，并解压运行GoodSync-Setup.exe 进行软件安装，软件默认安装为英文，如果要安装简体中文版，在安装时注意选择语言为：simpchinese项，安装完毕后运行GoodSync程序。 2、将你电脑的系统时间设置到2011年。 3、如下图所示，在软件主界面依次点击选择：帮助→ 激活专业版。
大数据日志分析系统
点击文章中飘蓝词可直接进入官网查看 大数据日志分析系统 大数据时代，网络数据增长十分迅速。大数据日志分析系统是用来分析和审计系统及 事件日志的管理系统，能够对主机、服务器、网络设备、数据库以及各种应用服务系统等 产生的日志进行收集和细致分析，大数据日志分析系统帮助IT管理员从海量日志数据中准确查找关键有用的事件数据，准确定位网络故障并提前识别安全威胁。大数据日志分析系 统有着降低系统宕机时间、提升网络性能、保障企业网络安全的作用。 南京风城云码软件公司（简称：风城云码）南京风城云码软件技术有限公司是获得国 家工信部认定的“双软”企业，具有专业的软件开发与生产资质。多年来专业从事IT运维监控产品及大数据平台下网络安全审计产品研发。开发团队主要由留学归国软件开发人员 及管理专家领衔组成，聚集了一批软件专家、技术专家和行业专家，依托海外技术优势， 使开发的软件产品在技术创新及应用领域始终保持在领域上向前发展。 审计数据采集是整个系统的基础，为系统审计提供数据源和状态监测数据。对于用户 而言，采集日志面临的挑战就是：审计数据源分散、日志类型多样、日志量大。为此，系 统综合采用多种技术手段，充分适应用户实际网络环境的运行情况，采集用户网络中分散 在各个位置的各种厂商、各种类型的海量日志。 分析引擎对采集的原始数据按照不同的维度进行数据的分类，同时按照安全策略和行 为规则对数据进行分析。系统为用户在进行安全日志及事件的实时分析和历史分析的时候 提供了一种全新的分析体验——基于策略的安全事件分析过程。用户可以通过丰富的事件分析策略对的安全事件进行多视角、大跨度、细粒度的实时监测、统计分析、查询、调查、追溯、地图定位、可视化分析展示等。
东莞二期投标文件管理软件操作手册V2.0.0.3
投标文件管理软件（V2.0.0.3） 用 户 使 用 手 册 深圳市斯维尓科技有限公司 二〇一三年三月五日
目录 1引言 (3) 2 程序运行环境 (4) 3 程序安装 (4) 4 软件启动 (9) 5软件整体说明 (12) 6 软件操作说明 (15) 6.1导入查看招标文件 (15) 6.2新建投标文件 (16) 6.3投标文件的管理功能 (23) 6.4校对工程量清单 (29) 6.5转换投标文件 (30) 6.6 电子签章 (32) 6.7生成投标文件 (34) 6.8查看数字签名信息 (41) 7 程序卸载 (42)
1引言 编写本手册的主要目的是为东莞市建设工程交易中心电子评标系统的投标文件管理软件的使用提供帮助。 投标文件管理软件主要提供给投标单位使用。投标单位通过投标文件管理软件将工程招标文件的一些主要内容导出，根据招标要求制作投标文件；加入已经制作好的工程投标文件所包含的所有文档（包括：技术标文件、工程量清单、工程图纸以及其它文件等），并进行管理，对文件包进行CA数字签名以防篡改，并生成压缩加密的电子投标文件包的功能。 投标文件管理软件的使用总体流程如下图所示：
2 程序运行环境 ?硬件环境：CPU: P4 2GHZ 内存2G,硬盘80GB ?软件环境：Windows 2000/XP/Windows Server 2003 ?软件支持：OFFICE2007+PDF转换插件/OFFICE2010 ?网络环境：带宽10/100Mbps 3 程序安装 东莞市建设工程交易中心网站(https://www.wendangku.net/doc/4a11166881.html,/)上下载最新安装包，点击安装程序，安装程序引导用户进行系统安装，主要有以下步骤： 一、启动安装程序，进入安装系统欢迎界面。如下图：
个人文件同步备份FILEGEE
软件简介： FileGee之软件主界面(图一) FileGee个人文件同步备份系统是一款优秀的文件同步与备份软件。它集文件备份、同步、加密、分割于一身。协助个人用户实现硬盘之间，硬盘与移动存储设备之间的备份与同步。强大的容错功能和详尽的日志、进度显示，更保证了备份、同步的可靠性。高效稳定、占用资源少的特点，充分满足了用户的需求。不需要额外的硬件资源，便能搭建起一个功能强大、高效稳定的全自动备份环境，是一种性价比极高的选择。 一.软件安装 FileGee个人文件同步备份系统在使用前须对其进行安装才可进行使用，软件须按照提示进行安装，软件安装过程如下图所示： FileGee之接受安装协议(图二) FileGee之选择安装目录(图三) FileGee之完成安装(图四) 二.软件使用 FileGee在完成安装后双击桌面图标即可启动该软件，用户如要创建备份，即可点击软件左上角处的新建任务按钮来创建新任务，软件提供多种任务类型，如单向同步，双向同步，镜像同步，更新同步等等，用户鼠标停留在任务类型上即可看到相关的解释说明，如下图所示： FileGee之新建任务(图五) 用户在选择创建备份任务类型后，即可点击下一步按钮，点击后软件会自动弹出窗口，用户需在窗口中设置要进行备份的文件夹所在位置，如下图所示： FileGee之设置备份文件夹(图六) 设置完要进行备份的文件夹后，我们还需要对备份文件的存储位置进行设置保存，另外为了节省空间我们还可以对文件设置是否进行压缩，如下图所示：
FileGee之备份文件保存(图七) 在设置完毕后我们即可点击下一步按钮，在后面的设置选项中我们还可以对备份的文件进行详细设置，如是否包含源目录的子目录，还可以根据文件名对要备份的文件进行过滤，也可以对文件进行过滤设置，如下图所示： FileGee之备份设置(图八) 设置完毕后，我们即可点击软件上侧列表中的开始按钮对文件进行备份，，另外还可以点击软件上侧的定时自动功能设置定时对文件夹进行自动备份，如下图所示： FileGee之备份任务(图九) 小结：FileGee作为一款免费得文件夹自动同步备份工具，不但功能上比较强大，在使用上也是非常的方便，如果您也需要一款文件备份工具的话，那么就来试试FileGee吧，只需简单几步就可以完成文件夹同步备份，非常方便!