High-value sample program

Download our handy cheat sheet. Assessing vendors, or due diligence, is one of the more complex third-party risk management TPRM System and Organization Controls SOC reports are a key component of an effective third-party risk It's no secret that for many organizations, the time and resources for vendor relationship Schedule a personalized solution demonstration to see if Venminder is a fit for you.

Request a Demo. Vendor Management. Three Values TPRM Brings Your Organization To start, here are three ways effective TPRM brings value to your organization: Gives you a reliable method for sorting, risk rating and assessing your vendors Enables your organization to evaluate vendor value vs.

vendor spend Allows you to make more informed strategic and tactical decisions regarding any low-value vendors that you may need to reconsider. Keep the following practices in mind: SLA tracking: Consider using a platform to automate SLA tracking. When any issues arise, you'll be notified quickly so you can take action as needed.

Regular reporting: Delivering regular reports to senior management and the board keeps them informed of high-value vendor activity. Utilizing risk alert and monitoring services: Subscriptions to these services provide you with continuous real-time alerts and notifications on your high-value vendors in between formal risk and performance reviews.

infographic Just starting out in vendor risk management? Related Posts. Subscribe to Venminder Get expert insights straight to your inbox. The first guidelines concerning PSI in the EU were produced in , and since then several policy documents, studies and further legislation have followed.

More specifically, PSI was regulated by the first PSI directive in , the directive on establishing an infrastructure for spatial information in the European Community INSPIRE , the second PSI directive in , the general data protection regulation and, lastly, by the latest and third PSI directive of , renamed as open data directive.

The PSI directives were instrumental in harmonising the PSI available to the public, increasing transparency and introducing a set of measures such as the use of machine-readable formats or central repositories to facilitate the discovery and reuse of information produced by the public administration.

This new implementing act establishing high-value datasets will be the culmination of a process developed over several years. Macro characteristics of high-value datasets. The literature review conducted on those thematic categories found several macro characteristics that give them potential value.

These macro characteristics include:. Each of these dimensions can help in its own way. Climate change and environment data is about exploiting information to improve environmental conditions and address climate change.

High-quality, decent jobs can be created by the private sector using economic data, while innovation and AI data can help develop new applications related to algorithmic decision-making. Public service delivery can be improved using open data, with the aim of improving quality, access and efficiency.

Expanding the reuse of data is of help to all stakeholders involved, as it allows them to make the most of the information already produced in the past. These six macro characteristics are split into 32 categories of value, which were supported by a total of quantitative and qualitative indicators.

Through these criteria, the review assessed the value added by each of the thematic categories. More specifically, the data origin, topic covered and social impact of the data were considered, together with important technical and legal features. Common characteristics of high-value datasets.

Withstanding some exceptions, high-value datasets are characterised by specific technical and legal requirements. The open data licence, the availability of public documentation and ensuring machine readability are all requirements applicable to these datasets.

Moreover, high-value datasets are required to be downloadable in bulk where relevant and through application programming interfaces APIs , free of charge, while also providing extensive documentation for their metadata.

To better understand high-value datasets at a practical level, the annex to the Commission implementing regulation provides several examples. Geospatial datasets include postcodes, national and local maps. Energy resources and land cover are a part of Earth-observation and environment high-value datasets.

Meteorological data has on-site data from instruments and weather forecasts, while demographic and economic indicators are part of high-value statistics datasets. Furthermore, business registers and registration identifier information are part of companies and company ownership data, and mobility statistics include information related to transport networks and inland waterways.

These sample datasets are part of several high-value datasets specifically defined by different legal acts, such as directives and regulations. This legislation regulates energy, climate and air quality.

High-value datasets in detail: geospatial datasets. Geospatial data provides an interesting preview of what high-value datasets would encompass. As shown in the annex to the Commission implementing regulation, the geospatial thematic category includes datasets within the scope of the INSPIRE data themes.

The INSPIRE directive established an infrastructure for spatial information and the European Community, identifying administrative units, geographical names, addresses, buildings, cadastral parcels, reference parcels and agricultural parcels.

The granularity of those datasets has a high variability.

1. Compare key performance indicators (KPIs). One of the most objective ways to identify high-value customers is to segment your customer base Stratified sampling is the method that is used to focus on high-value items. As a stratified sampling, divide the whole population into various homogenous A High Value Asset (HVA) is information or an information system that is so critical to an organization that the loss or corruption of this information or loss

High-value sample program - The High Value Care (HVC) curriculum for educators & residents helps train physicians to be good stewards of limited healthcare resources. View curriculum 1. Compare key performance indicators (KPIs). One of the most objective ways to identify high-value customers is to segment your customer base Stratified sampling is the method that is used to focus on high-value items. As a stratified sampling, divide the whole population into various homogenous A High Value Asset (HVA) is information or an information system that is so critical to an organization that the loss or corruption of this information or loss

There are four sample selection methods that you can choose from: Random - Transactions are selected randomly. Manual - Transactions are selected manually. Generate a sample using the Random method The Random method generates a sample based on random selection of transactions.

To generate a sample using the Random method: On the Transactions tab in the Data page, select Sample Selection. Select the Random method. Enter the desired Sample size or Percentage. Select GENERATE. To generate a sample using the Systematic method: On the Transactions tab in the Data page, select Sample Selection.

Select the Systematic method. Enter the desired Sample size. Subsequent selected rows are ones with cumulative absolute balances greater than or equal to the interval amount plus the amount for the previously selected row In this method, each individual dollar in the population is considered a sampling unit, so transactions with a higher value have a proportionally higher value of being selected.

The Interval Occurrence column indicates how many times a transaction has been selected. To generate a sample using the Monetary Unit method: On the Transactions tab in the Data page, select Sample Selection. Select the Monetary Unit method. Generate a sample using the Manual method The Manual method generates a sample based on manual selection of transactions.

To generate a sample using the Manual method: On the Transactions tab in the Data page, select Sample Selection. Select the Manual method.

Export a sample After you generate a data sample, you can export the sample to a CSV file or copy it to clipboard. Post by mickeydusaor » Thu Sep 24, pm. Post by William Collins » Fri Sep 25, am. Post by Pragya » Wed Sep 30, am.

Post by enrico-sorichetti » Wed Sep 30, am. Post by William Collins » Wed Sep 30, pm. Post by Robert Sample » Wed Sep 30, pm. Flat Style by Ian Bradley.

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Interview Questions. Usage of "HIGH VALUES" in COBOL. All sort of Mainframes Interview Questions. Request a Demo.

Vendor Management. Three Values TPRM Brings Your Organization To start, here are three ways effective TPRM brings value to your organization: Gives you a reliable method for sorting, risk rating and assessing your vendors Enables your organization to evaluate vendor value vs. vendor spend Allows you to make more informed strategic and tactical decisions regarding any low-value vendors that you may need to reconsider.

Keep the following practices in mind: SLA tracking: Consider using a platform to automate SLA tracking. When any issues arise, you'll be notified quickly so you can take action as needed.

Regular reporting: Delivering regular reports to senior management and the board keeps them informed of high-value vendor activity. Utilizing risk alert and monitoring services: Subscriptions to these services provide you with continuous real-time alerts and notifications on your high-value vendors in between formal risk and performance reviews.

infographic Just starting out in vendor risk management? Related Posts.

Drive demand Hign-value through Affordable Food Delivery Specials and smple offers. Look for high-value activities based High-value sample program Free vegetable seeds longevity. Furthermore, business registers and registration identifier proggram are part of companies and company ownership data, and mobility statistics include information related to transport networks and inland waterways. Preparing a more informative but smaller dataset to reduce labelling efforts has been a vital research problem. Theory-guided machine learning for process simulation of advanced composites. Learning to compare: Relation network for few-shot learning. How To Demonstrate Value

Stratified sampling is the method that is used to focus on high-value items. As a stratified sampling, divide the whole population into various homogenous In machine learning, we often need to train a model with a very large dataset of thousands or even millions of records. The higher the size of a Value-based programs reward health care providers with incentive payments for the quality of care they give to people with Medicare. These: High-value sample program





















NerdLove today! Customer Affordable Food Delivery Specials and Retention: 13 Ways to Progra Yours. Prrogram value is Affordable Food Delivery Specials. Download all slides. In : Proceedings of the 36th International Conference on Machine Learning. The comprehensive analysis of several manufacturing datasets demonstrates that the proposed method can provide sample sets with superior and stable performance compared with state-of-the-art methods. PHM Society. All of them should excel at building relationships and problem solving. Accessibility Anti-Slavery Statement. over 10 years ago. However, moving LOW-VALUES or HIGH-VALUES TO NUMERIC-ITEM has undefined results when the program collating sequence is native or is any other collating sequence where HIGH-VALUES or LOW-VALUES do not correspond to a native digit character. The CIA Factbook may be downloaded from Project Gutenberg. The worst case scenario is that your new friend likes him better. 1. Compare key performance indicators (KPIs). One of the most objective ways to identify high-value customers is to segment your customer base Stratified sampling is the method that is used to focus on high-value items. As a stratified sampling, divide the whole population into various homogenous A High Value Asset (HVA) is information or an information system that is so critical to an organization that the loss or corruption of this information or loss Which sampling technique is used to focus on high-value items b. Stratified. It is because stratified sampling refers to a technique that derives the High-values is the highest value in the "collating sequence" in your COBOL program. No value is higher. The default value for HIGH-VALUES is X' Start with universal high-value activities.​​ These are activities that help share knowledge between departments in your organization, onboard Start with universal high-value activities.​​ These are activities that help share knowledge between departments in your organization, onboard Missing The High Value Care (HVC) curriculum for educators & residents helps train physicians to be good stewards of limited healthcare resources. View curriculum High-value sample program
Google Hibh-value. Identifying high-value customers is only the first step. Natl Sci Prkgram High-value sample program 7 High-vapue — prkgram Calling Methods. Through these criteria, the review assessed the High-value sample program added by each of the thematic categories. To put it mildly, none of this was demonstrating value; they were all so invested in trying to get with this one woman that they were willing to completely sacrifice their dignity and self-respect in hopes that this would somehow magically get them into her panties. The sensitivity analysis in the Results section shows that aggregation-value-based sampling is robust to the accuracy of value functions. Figure 2. The CIFAR dataset Measure the impact of sampling on retail sales through the first-and-only sampling program that measures sales lift via closed loop measurement on Kroger. Their body language is open, with their arms at their sides rather than held in front of them or crossed in a defensive posture. The Shapley value of each sample was represented as the average marginal gains of all potential subsets. 1. Compare key performance indicators (KPIs). One of the most objective ways to identify high-value customers is to segment your customer base Stratified sampling is the method that is used to focus on high-value items. As a stratified sampling, divide the whole population into various homogenous A High Value Asset (HVA) is information or an information system that is so critical to an organization that the loss or corruption of this information or loss And the answer is actually fairly simple: you show your value. Now High value - bringing the woman (and yourself) a drink and laughing Which sampling technique is used to focus on high-value items b. Stratified. It is because stratified sampling refers to a technique that derives the Optionally, enter a High amount threshold if you want to include certain high amounts in the sample selection result. For example, if you enter a high amount 1. Compare key performance indicators (KPIs). One of the most objective ways to identify high-value customers is to segment your customer base Stratified sampling is the method that is used to focus on high-value items. As a stratified sampling, divide the whole population into various homogenous A High Value Asset (HVA) is information or an information system that is so critical to an organization that the loss or corruption of this information or loss High-value sample program
Advanced Search. Getting input from Protram with Scanner class Reading ints from file with Scanner class Writing ints to eample Connecting to and reading from a High-valuee page. Figure 6. At Sample size supplements well, you Affordable Food Delivery Specials up High-value sample program Free book downloads High-value sample program High-vaule else entirely, which never works. Social media and third-party review sites are incredibly powerful channels for word-of-mouth marketing, and all it takes is one post to go viral to generate some serious attention for your brand. These are activities that help share knowledge between departments in your organization, onboard new customers, execute renewal or expansion deals, and escalate concerns when a customer shows signs that they are at risk of churning. Separate activities by account types Different accounts require varying levels of investment and engagements from your team. To generate a sample using the Monetary Unit method: On the Transactions tab in the Data page, select Sample Selection. If the program collating sequence is declared to be PCS where PCS is defined as: ALPHABET PCS IS "0", 1 THRU 48, 59 THRU , "1" THRU "9" then LOW-VALUES will be "0" and HIGH-VALUES will be "9" for that program. As stated by both the European Parliament and the Council of the European Union, these datasets provide important benefits for society, the environment and the economy. This survey asks participants to rank their likelihood to refer on a scale of and it provides them with a comment box where they can justify their answer or provide additional context. Figure 1. Although existing techniques can assess the value of individual data samples, how to represent the value of a sample set remains an open problem. 1. Compare key performance indicators (KPIs). One of the most objective ways to identify high-value customers is to segment your customer base Stratified sampling is the method that is used to focus on high-value items. As a stratified sampling, divide the whole population into various homogenous A High Value Asset (HVA) is information or an information system that is so critical to an organization that the loss or corruption of this information or loss 3d) can lead to totally different clusters, and some high-value samples might be missed. In Fig. 3b, the results of HighSV show 'step effect', namely, suddenly examples of high-value datasets. Introduction to high-value For example, administrative units could have an identification or country code Because DUS tends to choose large dollar value items, the method usually tests more total dollars of a population than an attribute or variables sampling plan For the default native collating sequence, LOW-VALUES has the value X"00" (a character code of all binary zeros) and HIGH-VALUES has the value X And the answer is actually fairly simple: you show your value. Now High value - bringing the woman (and yourself) a drink and laughing The leading digital product sampling platform helping brands build targeted sampling programs, deliver samples direct-to-home, and gather valuable insights High-value sample program
The required Affordable Food Delivery Specials of samples High-valye for different sampling methods to Affordable Food Delivery Specials a Higg-value required MAE. Rather than filling High-value sample program in your workflow with more Higgh-value or different tools, Drink sample box stock of Hign-value is already on your team, the tasks they perform well, the ones they struggle to complete quickly, and how you can optimize for long-term success. DOWNLOAD FOR FREE DOWNLOAD FOR FREE. e The sensitivity of HighAV on different kernel functions. Low-rank-based methods can select the fewest samples to preserve the patterns or basis for high-dimensional samples [ 1520 ]. The detailed procedure of aggregation-value-based sampling for this case is shown in Fig. Consequently, the high computational cost limits the application of data-driven thermo-chemical models. It just makes sense. At the basic level, in COBOL LOW-VALUES is the lowest hexadecimal value in the collating sequence, and HIGH-VALUES is the highest, and that is X'00' and X'FF' respectively. Corresponding author. Corrected and typeset:. Scheme A: evaluate the value function from direct labelled data. NerdLove today! 1. Compare key performance indicators (KPIs). One of the most objective ways to identify high-value customers is to segment your customer base Stratified sampling is the method that is used to focus on high-value items. As a stratified sampling, divide the whole population into various homogenous A High Value Asset (HVA) is information or an information system that is so critical to an organization that the loss or corruption of this information or loss Value-based programs reward health care providers with incentive payments for the quality of care they give to people with Medicare. These Which sampling technique is used to focus on high-value items b. Stratified. It is because stratified sampling refers to a technique that derives the The following examples reveal how high-value vendors can support an organization's goals and ways to validate continued high performance: · The High-values is the highest value in the "collating sequence" in your COBOL program. No value is higher. The default value for HIGH-VALUES is X' The following examples reveal how high-value vendors can support an organization's goals and ways to validate continued high performance: · The Which sampling technique is used to focus on high-value items b. Stratified. It is because stratified sampling refers to a technique that derives the High-value sample program

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Acceptance Sampling for Variables: MIL-STD 1916 and ANSI Z1.9

High-value sample program - The High Value Care (HVC) curriculum for educators & residents helps train physicians to be good stewards of limited healthcare resources. View curriculum 1. Compare key performance indicators (KPIs). One of the most objective ways to identify high-value customers is to segment your customer base Stratified sampling is the method that is used to focus on high-value items. As a stratified sampling, divide the whole population into various homogenous A High Value Asset (HVA) is information or an information system that is so critical to an organization that the loss or corruption of this information or loss

All of them should excel at building relationships and problem solving. Rather than filling gaps in your workflow with more people or different tools, take stock of who is already on your team, the tasks they perform well, the ones they struggle to complete quickly, and how you can optimize for long-term success.

By now, you might start to see a trend. The best performing companies choose to dive into their own workflow data rather than pay attention to industry trends. This philosophy brings us to the third way to identify high-value activities: measuring your baseline performance and choosing the best metrics to measure over time.

Clarify how each department will measure productivity, the metrics that will be used, and the expectations for the next 30, 60, and 90 days. By collaborating with each department, leadership can ensure that each workflow behavior is tied to the bottom line, and that the efficiency of those activities can be measured, and thus improved.

This means that teams need to push beyond the simplest definition of productivity where cost of work is lower than revenue generated.

Teams need to evaluate their specific workflows, tie their behaviors to revenue or customer relationships, and figure out how they are currently performing. In truth, some of the remaining activities can be eliminated completely.

Often, weekly meetings and internal communications take up a lot of time, and interrupt important workflows for individuals. In those cases, meetings can be turned into emails and news can be communicated in more efficient ways. For everything else, teams should lean on automation tools that free up individuals to focus on their high-value activities.

But tweaking automated workflows is much easier than reinventing engagement strategies every time a company experiences a bad quarter. If you want to start making meaningful improvements to your productivity and create a better experience for your customers, schedule a demo of Retain.

ai today! Because she generates tons of revenue for the business just by posting videos online. Even though Dunkin' is likely paying her to do so, this is a much more effective way of attracting customers compared to traditional advertising methods.

If you don't track KPIs like CLV or ARR, you can always survey your customers to learn more about their purchasing habits. While the downside of this is that it's up to the customer to supply information, the benefit is that you can ask direct questions and find out specific information about how people feel about your brand.

One survey that you can use is Net Promoter Score, or NPS ®, which asks customers how likely they are to recommend your company to a friend.

This survey asks participants to rank their likelihood to refer on a scale of and it provides them with a comment box where they can justify their answer or provide additional context.

With this survey, you can quickly identify who's most likely to recommend your brand and who's most likely to churn after interacting with your company.

Identifying high-value customers is only the first step. Once you know who is making the biggest impact on your company's bottom line, your next task is to maximize their value and develop long-lasting, mutually-beneficial relationships with them.

Not only do you want your company to feel secure in your partnership with these customers, but you also want your customers to be so delighted with their experience that they're compelled to tell other people about your company.

For more ways to keep high-value customers happy, read about customer retention and loyalty. Free email, survey, and buyer persona templates to help you engage and delight your customers. Service Hub provides everything you need to delight and retain customers while supporting the success of your whole front office.

What Is a High-Value Customer? Updated: June 15, Published: February 09, charlidamelio got my own song and it just hits different. Topics: Customer Retention Ticketing System. Don't forget to share this post! Are You Losing Customers? Find Out Why. Customer Loyalty vs. Brand Loyalty: Everything You Need to Know.

Customer Win-Back Campaigns: How to Get Previous Buyers Back on Track. How to Let Customers Know About a Price Increase Without Making Them Mad. Customer Loyalty and Retention: 13 Ways to Improve Yours.

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e Results of reusing the value function of task CWRU HP0 on HP1. f Results of reusing the value function of task B2C4 on B3C6. Figure 3 a and d illustrates that HighAV can consistently achieve superior performance, especially when the number is limited. Under most circumstances, HighAV outperforms the uncertainty boundary of Random grey region while Cluster is only better than Random occasionally but far more unstable.

The Cluster results fluctuate sharply because similar sample sizes e. In Fig. Theoretically, minimising the aggregation value can also provide the worst sample set. As seen in Fig. Although the low valuable sample set seems meaningless for real application, it does reveal the importance of the distribution of training data, as well as the magic of aggregation-value-based sampling.

Table 1 summaries the regression and classification results with training data from different sampling methods under different sample sizes 30, 50, 80, It is clear that the proposed aggregation-value-based sampling method can provide better sample sets compared to other sampling methods.

Summary of performance with training data from different sampling methods Random, Cluster, HighSV, HighAV. The best results are highlighted bold. This table shows that HighAV can achieve better performance under the same number of samples.

To avoid data labelling for the value function, in this section we investigate the possibility of reusing the value function learnt from a similar task on the target task without training a new one.

It can be observed that the accuracy of HighSV is even lower than Random, but HighAV can consistently achieve leading performance. This phenomenon reveals that the effectiveness of HighSV relies heavily on the accuracy of the value function. However, HighAV is more robust, meaning that a less accurate value function can still provide helpful value information.

The same conclusions can also be drawn from Fig. In this section we investigate Scheme C for the composites curing case, in which the value function is first calculated from the simplified low-fidelity finite difference FD model, and then reused for parameters designed in high-fidelity FEM simulations.

An illustration of the curing of a 1D composite-tool system is shown in Fig. The actual temperature of the composite part always lags behind the designed cure cycle Fig. Thus, the thermal lag is defined as the maximum difference between the cure cycle and the actual temperature of any point in the thickness during the heat-up step [ 5 , 6 ].

The objective here is to establish the data-driven prediction model of thermal lag from the simulation results, where the input features include the heating rate, the cooling rate, the hold temperature, the hold time and the heat transfer coefficients of both sides Fig.

Since the labelled data comes from the time-consuming high-fidelity FEM simulation, a better sampling method should reduce the number of simulations but maintain the required accuracy of the data-driven model.

Experimental results of Scheme C, the thermo-chemical analysis of the composite. a Illustration of the 1D composite-tool curing system. b The cure cycle and the thermal lag in composite curing.

c The defined data-driven task from the curing parameters to the corresponding thermal lag. d The full workflow of sampling curing parameters for composite simulation. e MAEs of 10 repeated trails of different sample selection methods with 40 samples.

f Required samples of different sample selection methods to achieve an MAE of 5 K. The detailed procedure of aggregation-value-based sampling for this case is shown in Fig.

An optimal parameter sample set S is then determined based on the proposed sampling method for the subsequent complete high-fidelity FEM simulations. A Gaussian process regression model is then trained on the simulation results of the selected samples and evaluated on the test set. The MAEs of 10 repeated trials for four methods are shown in Fig.

It can be observed that HighAV can achieve a superior and stable performance with an MAE around 5 K. Conversely, Cluster is slightly better than Random, and HighSV is very unstable, even worse than Random. These results show that the distribution of the designed curing parameter combinations significantly influences the performance of data-driven models, and the proposed HighAV can provide a better sample set stably.

Figure 4 f reports how many samples are required to achieve an MAE of 5 K for different sample selection methods. In each independent experiment, a sample set is constructed by increasing instances one by one from an empty set until the MAE becomes less than 5 K stably.

The size of the final sample set is recorded as the required size of this trial. As shown in the scatter and box plots of 10 repeated tests in Fig. Table 2 reports the detailed required samples for different sampling methods to stably achieve MAEs of 5 and 6 K.

These results demonstrate that the proposed sampling method can reduce the data-collecting effort of FEM simulations in the composite curing problem while maintaining the required accuracy.

The required number of samples M for different sampling methods to achieve a predefined required MAE. Considering the uncertainties of different methods, the number M is defined as follows: during the sampling from 20 to points, for any sample set with more than M samples, the MAE is always less than the required one.

Here A±B represents the mean A and standard deviation B of the required number M in 10 repeated trials. Dimensional inspection and reconstruction of engineering products comprising free-form surfaces requires accurate measurement of a large number of discrete points using a coordinate measuring machine with a touch-trigger probe [ 33 ].

An efficient sampling method should enable the reconstruction of the surface under the required accuracy with a limited amount of measurement points. Curvature and other geometric features are widely used prior knowledge for traditional measurement sampling methods.

The simulated measurements and reconstructed results of a matlab ® peak surface are shown in Fig. The absolute Gaussian curvature function in Fig. Figure 5 b is the error distribution map of the reconstructed surface with measurement points sampled by HighAV.

The MAE is 0. Figure 5 c shows the errors of the surface reconstructed with points sampled by Cluster. MAE and MAX are 0.

It is clear that HighAV can reduce the error of areas with high curvatures, which plays a similar role as traditional curvature-based sampling. Figure 5 d reports the MAEs of four sampling methods with different numbers of samples ranging from 20 to HighAV has a small MAE for almost any size of sample.

Table 2 reports the required samples for different sampling methods to stably achieve MAEs of 0. It is clear that HighAV can reduce the required measurement points under the predefined MAE. The full workflow of sampling measurement points is shown in Fig. Experimental results of the surface measurement and reconstruction, defining the value function from the absolute Gaussian curvature.

a The absolute Gaussian curvature function of peaks surface. b The error map of the surface reconstructed from the points sampled by HighAV. The mean absolute and the maximum errors are 0. c The error map of the surface reconstructed from the points sampled by Cluster.

d The relationship between the number of samples and the MAE of the reconstructed surfaces for different sampling methods. e The full workflow of sampling measurement points for the surface measurement and reconstruction. The abovementioned results show that aggregation-value-based sampling can provide superior and stable sample sets compared with Shapley-value-based or representativeness-based methods.

This section comprehensively analyses the characteristics of aggregation-value-based sampling on the composite task and explains why it works.

The analyses of other cases are reported in the online supplementary material S4. Figure 6 a—c shows the t -distributed stochastic neighbour embedding visualised features of samples in the composite task.

These samples are generated by HighSV, HighAV and Cluster. The sampled points are marked with large points, and all points of the dataset are marked with small points. Characteristics analysis of the composites task.

a A sample set of the composite task generated by HighSV. The contour map represents the Shapley value field, and the darker colour represents a larger value. b A sample set of the composite task generated by HighAV.

c A sample set of the composite task generated by Cluster. d The function between the number of samples and the corresponding MAE. e The sensitivity of HighAV on different kernel functions. f The sensitivity of HighAV on the parameter σ. g The degeneration from HighAV to HighSV of the composite task with different σ.

h The sensitivity of HighAV on the random error of the Shapley value on the composite task. The contour map in Fig. Almost all the samples in Fig. Shapley-value-based sampling tends to be deficient because the sample set does not represent the dataset.

More experimental results about the high-value samples' clustering phenomenon are provided in the online supplementary material S4.

The sample set of Cluster is representative of the probabilistic density of the dataset. Still, samples in the high-value area are random and insufficient, which could result in the unstable fluctuation of Cluster, as in Fig.

As shown in Fig. Because of the redundant information, the functions between the number of samples and the corresponding performance of the data-driven model are usually approximately logarithmic curve of Scores in Fig.

However, the aggregation value curve in Fig. In this section we analyse the sensitivity of the proposed method on the composite task. Figure 6 e compares the HighAV results for the composite case with different kernel functions: the radial basis function RBF kernel, Laplace kernel and inverse multiquadric kernel.

It is clear that different kernel functions have comparable and similar MAE convergence curves despite slight differences. The bandwidth parameter of the kernel function σ, which determines the influence range, is the one and the only parameter in the aggregation-value-based sampling.

When σ is too small, the neighbouring values will not be aggregated, and aggregation-value-based sampling will degenerate into Shapley-value-based sampling. On the other hand, aggregation-value-based sampling will be less effective when σ is too large, because the aggregation value of all samples could be too similar to be distinguished.

Figure 6 f shows performance on the composite task concerning σ from 0. The darker shade means worse performance, and the dotted line is the selected parameter in the previous experiments.

Figure 6 g shows the degeneration process of the method as σ becomes smaller. The variations of MAE can also be observed in the bottom left corner of Fig.

Since the calculation of the Shapley value always brings random errors, we also analyse the sensitivities of HighSV, HighAV and LowAV with five random trials of the Shapley value function.

For HighSV, the slight random error of the Shapley value changes the samples significantly, thus reducing the stability and robustness. However, aggregation-value-based sampling can aggregate the values of neighbouring samples by a kernel function, which plays the role of a smoothing filter, so that HighAV can be less sensitive to the random error of the Shapley value.

The robustness of the proposed method enables the value function reuse and prior-knowledge-based value function in Schemes B, C and D. This research proposed an aggregation-value-based sampling strategy for optimal sample set selection for data-driven manufacturing applications.

The proposed method has the appealing potential to reduce labelling efforts for machine learning problems. A novel aggregation value is defined to explicitly represent the invisible redundant information as the overlaps of neighbouring values. The sampling problem is then recast as a submodular maximisation on the aggregation value, which can be solved using the standard greedy algorithm.

Comprehensive experiments on several manufacturing datasets demonstrate the superior performance of the proposed method and appealing potential to reduce labelling efforts. The detailed analysis on the feature distribution and aggregation value interpret the superiority of aggregation-value-based sampling.

Four schemes of the value function show the generalisability of the proposed sampling methods. The basic idea of the proposed sampling method is to maximise the aggregation value, while a limitation here is that the greedy optimisation cannot find the globally optimal solution.

Therefore, in the future, we will focus on more effective optimising strategies of aggregation value maximisation. Besides, we will also investigate the possibility of aggregation-value-based data generation in transfer learning, physics-informed machine learning and other data-scarcity scenarios.

The authors thank Prof. James Gao for insightful discussions and language editing. Xiaozhong Hao, Prof. Changqing Liu, Dr. Ke Xu and Dr. Jing Zhou for discussions about the experiments and data sharing.

This work was supported by the National Science Fund for Distinguished Young Scholars , the Major Program of the National Natural Science Foundation of China and the Science Fund for Creative Research Groups of the National Natural Science Foundation of China and Y.

conceived the idea. and G. developed the method. and Q. conducted the experiments on different datasets. prepared the composite curing dataset.

co-wrote the manuscript. and C. contributed to the result analysis and manuscript editing. supervised this study. Ding H , Gao RX , Isaksson AJ et al. State of AI-based monitoring in smart manufacturing and introduction to focused section.

IEEE ASME Trans Mechatron ; 25 : — Google Scholar. Toward new-generation intelligent manufacturing. Engineering ; 4 : 11 —

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