scholarly journals Cost Estimating Using a New Learning Curve Theory for Non-Constant Production Rates

Forecasting ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 429-451 ◽  
Author(s):  
Dakotah Hogan ◽  
John Elshaw ◽  
Clay Koschnick ◽  
Jonathan Ritschel ◽  
Adedeji Badiru ◽  
...  
Keyword(s):  
Learning Curve ◽  
Department Of Defense ◽  
Learning Curves ◽  
Learning Rate ◽  
Traditional Learning ◽  
Cost Estimating ◽  
Cost Overruns ◽  
Learning Rates ◽  
New Learning ◽  
Curve Theory

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, namely Boone’s learning curve, was recently developed to model this phenomenon. This research confirms that Boone’s learning curve systematically reduced error in modeling observed learning curves using production data from 169 Department of Defense end-items. However, high amounts of variability in error reduction precluded concluding the degree to which Boone’s learning curve reduced error on average. This research further justifies the necessity of a diminishing learning rate forecasting model and assesses a potential solution to model diminishing learning rates.

Investigating the learning curve in endoscopic compared to microscopic myringotomy and ventilation tube insertion

2020 ◽  
Vol 134 (6) ◽  
pp. 497-500
Author(s):  
O Denton ◽  
A Daglish ◽  
L Smallman ◽  
S Fishpool
Keyword(s):  
Learning Curve ◽  
Learning Curves ◽  
Learning Rate ◽  
Middle Ear Surgery ◽  
Ventilation Tube ◽  
Ear Surgery ◽  
Steep Learning Curve ◽  
Learning Rates ◽  
Significant Difference ◽  
Rate Of Learning

AbstractObjectiveRate of learning is often cited as a deterrent in the use of endoscopic ear surgery. This study investigated the learning curves of novice surgeons performing simulated ear surgery using either an endoscope or a microscope.MethodsA prospective multi-site clinical research study was conducted. Seventy-two medical students were randomly allocated to the endoscope or microscope group, and performed 10 myringotomy and ventilation tube insertions. Trial times were used to produce learning curves. From these, slope (learning rate) and asymptote (optimal proficiency) were ascertained.ResultsThere was no significant difference between the learning curves (p = 0.41). The learning rate value was 68.62 for the microscope group and 78.71 for the endoscope group. The optimal proficiency (seconds) was 32.83 for the microscope group and 27.87 for the endoscope group.ConclusionThe absence of a significant difference shows that the learning rates of each technique are statistically indistinguishable. This suggests that surgeons are not justified when citing ‘steep learning curve’ in arguments against the use of endoscopes in middle-ear surgery.

scholarly journals Faster Convergent Artificial Neural Networks

2018 ◽  
Vol 17 (1) ◽  
pp. 7126-7132
Author(s):  
Dolores De Groff ◽  
Perambur Neelakanta
Keyword(s):  
Neural Networks ◽  
Input Data ◽  
Hessian Matrix ◽  
Research Work ◽  
Learning Curves ◽  
Fast Convergence ◽  
Learning Rate ◽  
The Novel ◽  
Learning Rates ◽  
Novel Method

Proposed in this paper is a novel fast-convergence algorithm applied  to neural networks (ANNs) with a learning rate based on the eigenvalues of the associated Hessian matrix of the input data.   That is, the learning rate applied to the backpropagation algorithm changes dynamically with the input data used for training.  The best choice of learning rate to converge to an accurate value quickly is derived. This newly proposed fast-convergence algorithm is applied to a traditional multilayer ANN architecture with feed-forward and backpropagation techniques.  The proposed strategy is applied to various functions learned by the ANN through training.  Learning curves obtained using calculated learning rates according to the novel method proposed are compared to learning curves utilizing an arbitrary learning rate to demonstrate the usefulness of this novel technique.  This study shows that convergence to accurate values can be achieved much more quickly (a reduction in iterations by a factor of  hundred) using the techniques proposed here.  This approach is illustrated in this research work with derivations and pertinent examples to illustrate the method and learning curves obtained. 

Using the learning curve theory in the investigation of on-site craft gangs’ blockwork construction productivity

2018 ◽  
Vol 8 (3) ◽  
pp. 267-280 ◽  
Author(s):  
Rex Asibuodu Ugulu ◽  
Stephen Allen
Keyword(s):  
Learning Curve ◽  
Research Study ◽  
Research Question ◽  
Construction Project ◽  
Learning Rate ◽  
Linear Regression Method ◽  
Production Environment ◽  
Content Type ◽  
Construction Productivity ◽  
Curve Theory

Purpose The purpose of this paper is to investigate how on-site blockwork craft gangs’ learning impacts productivity within the production environment on-site to optimise their productivity. Design/methodology/approach The research is adopting a quantitative method with the observation of seven craft gangs’ blockwork with an average of five members in each gang, using the learning curve model application in a 17-storey tri-tower construction project in Nigeria. The linear regression method was employed in the analysis stage of this study using labour-recorded productivity time input as the dependent variables. Findings The paper provides empirical insights about the significance of on-site craft gangs’ learning. The overall blockwork craft gangs learning observed at the site level shows an average learning rate of 94.21 per cent resulting in 5.79 per cent improvement gains. Research limitations/implications Due to the nature of the study and the research question, the observations in this research study were limited to FCDA construction project in Nigeria. The limitation of this scenario is that the research results may lack generalisability. Therefore, there is the need for further study on the learning rate. Practical implications This research study includes the implications for the development of on-site blockwork craft gangs learning; the significant impact of learning rate of 94.21 per cent resulting in 5.79 per cent improvement gain can be used in the planning and to fast track the productivity of craft gangs’ construction. Originality/value This paper identified the need to improve construction productivity through craft gangs’ on-site learning with the application of the learning curve theory.

scholarly journals Unpredictability vs. volatility and the control of learning

2020 ◽  
Author(s):  
Payam Piray ◽  
Nathaniel D Daw
Keyword(s):  
Anxiety Disorders ◽  
Computational Neuroscience ◽  
Rate Of Change ◽  
Learning Rate ◽  
Compensatory Behaviors ◽  
Learning Rates ◽  
New Learning ◽  
Two Factors ◽  
Show Evidence ◽  
The Brain

Influential research in computational neuroscience has stressed the importance of uncertainty for controlling the speed of learning, and of volatility, i.e. the inferred rate of change, in this process. Here, we investigate a neglected feature of these models: learning rates are jointly determined by the comparison between volatility and a second factor, unpredictability, which reflects moment-to-moment stochasticity. Like volatility, unpredictability can vary and must be estimated by the learner, but much previous research has focused on estimation of volatility while unpredictability is assumed fixed and known. We introduce a new learning model, in which both factors are learned from experience. We show evidence from behavioral neuroscience that the brain distinguishes these two factors and adjusts the learning rate accordingly. The model highlights the interdependency in inferences about volatility and unpredictability, which leads it to paradoxical compensatory behaviors if inference about either factor is damaged. This provides a novel mechanism for understanding pathological learning in amygdala damage and anxiety disorders.

scholarly journals LEARNING CURVE IN SINGLE-LEVEL MINIMALLY INVASIVE TLIF: EXPERIENCE OF A NEUROSURGEON

2017 ◽  
Vol 16 (4) ◽  
pp. 279-282 ◽  
Author(s):  
Samuel Romano-Feinholz ◽  
Sergio Soriano-Solís ◽  
Julio César Zúñiga-Rivera ◽  
Carlos Francisco Gutiérrez-Partida ◽  
Manuel Rodríguez-García ◽  
...  
Keyword(s):  
Blood Loss ◽  
Learning Curve ◽  
Hospital Stay ◽  
Learning Curves ◽  
Surgical Experience ◽  
Surgical Time ◽  
Intraoperative Bleeding ◽  
Single Level ◽  
Piecewise Regression ◽  
The Mean

ABSTRACT Objective: To describe the learning curve that shows the progress of a single neurosurgeon when performing single-level MI-TLIF. Methods: We included 99 consecutive patients who underwent single-level MI-TLIF by the same neurosurgeon (JASS). Patient’s demographic characteristics were analyzed. In addition, surgical time, intraoperative blood loss and hospital stay were evaluated. The learning curves were calculated with a piecewise regression model. Results: The mean age was 54.6 years. The learning curves showed an inverse relationship between the surgical experience and the variable analyzed, reaching an inflection point for surgical time in case 43 and for blood loss in case 48. The mean surgical time was 203.3 minutes (interquartile range [IQR] 150-240 minutes), intraoperative bleeding was 97.4ml (IQR 40-100ml) and hospital stay of four days (IQR 3-5 days). Conclusions: MI-TLIF is a very frequent surgical procedure due to its effectiveness and safety, which has shown similar results to open procedure. According to this study, the required learning curve is slightly higher than for open procedures, and is reached after about 45 cases.

Research on the Potential of Resource Saving and Emission Reducing in Anhui Province: Based on Environment Learning Curve

2011 ◽  
Vol 361-363 ◽  
pp. 1000-1004
Author(s):  
Qi Li
Keyword(s):  
Energy Consumption ◽  
Learning Curve ◽  
Water Consumption ◽  
Exponential Model ◽  
Learning Curves ◽  
Anhui Province ◽  
Resource Saving ◽  
Environmental Learning ◽  
Discharge Water ◽  
Resource And Environment

Based on the statistics data from 1990 to 2008 in Anhui province, this article selects four indexes which include energy consumption, SO2 discharge, water consumption and COD discharge per ten thousand yuan GDP, and establishes environment learning curves of energy consumption and air pollution, water consumption and pollution in Anhui province. According to these models, the potentials of resources saving and pollution reducing in different periods are calculated and analyzed, represented by the decrease of each index when the per capita GDP increase 1000 yuan. The result shows that: (1) With the growth of GDP per capital, each index falls by power exponential model which follows "environmental learning curve", illustrating that the burden of resource and environment was steady declining. (2) The potentials of saving energy resources and reducing pollution in Anhui province gradually descents from 1990 to 2008, illustrating that the reduction in marginal cost by the development of technology is becoming smaller and smaller, and the enhancement of resource using efficiency and pollution reducing efficiency is not unlimited.

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