Machine-Learning Based Analytics Applied to Stimulation Performance in the Utica Shale: Case Study and Lessons Learned

2018 ◽  
Author(s):  
Sebastien Perrier ◽  
Arnaud Delpeint ◽  
Zulibukaer Shawuti ◽  
Ashis Shrestha
Keyword(s):  
Machine Learning ◽  
Lessons Learned ◽  
Utica Shale

Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 19-32
Author(s):  
Daniel Buschek ◽  
Charlotte Anlauff ◽  
Florian Lachner
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
User Study ◽  
Concept Development ◽  
Lessons Learned ◽  
Design Tool ◽  
Learning Tools ◽  
Interface Elements ◽  
Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

scholarly journals Deep Learning for Recommender Systems: A Netflix Case Study

AI Magazine ◽  
2022 ◽  
Vol 42 (3) ◽  
pp. 7-18
Author(s):  
Harald Steck ◽  
Linas Baltrunas ◽  
Ehtsham Elahi ◽  
Dawen Liang ◽  
Yves Raimond ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Recommender Systems ◽  
Input Data ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Learning Models ◽  
Learning Methods ◽  
Recommendation Algorithms

Deep learning has profoundly impacted many areas of machine learning. However, it took a while for its impact to be felt in the field of recommender systems. In this article, we outline some of the challenges encountered and lessons learned in using deep learning for recommender systems at Netflix. We first provide an overview of the various recommendation tasks on the Netflix service. We found that different model architectures excel at different tasks. Even though many deep-learning models can be understood as extensions of existing (simple) recommendation algorithms, we initially did not observe significant improvements in performance over well-tuned non-deep-learning approaches. Only when we added numerous features of heterogeneous types to the input data, deep-learning models did start to shine in our setting. We also observed that deep-learning methods can exacerbate the problem of offline–online metric (mis-)alignment. After addressing these challenges, deep learning has ultimately resulted in large improvements to our recommendations as measured by both offline and online metrics. On the practical side, integrating deep-learning toolboxes in our system has made it faster and easier to implement and experiment with both deep-learning and non-deep-learning approaches for various recommendation tasks. We conclude this article by summarizing our take-aways that may generalize to other applications beyond Netflix.

scholarly journals Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

2021 ◽  
Author(s):  
Peter Burggraef ◽  
Johannes Wagner ◽  
Benjamin Heinbach ◽  
Fabian Steinberg ◽  
Alejandro Perez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Industry 4.0 ◽  
Predictive Analytics ◽  
Industrial Applications ◽  
Lessons Learned ◽  
Controlled Environment ◽  
Learning Models ◽  
Machine Learning Models

Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.

scholarly journals Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

2021 ◽  
Author(s):  
Peter Burggraef ◽  
Johannes Wagner ◽  
Benjamin Heinbach ◽  
Fabian Steinberg ◽  
Alejandro Perez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Industry 4.0 ◽  
Predictive Analytics ◽  
Industrial Applications ◽  
Lessons Learned ◽  
Controlled Environment ◽  
Learning Models ◽  
Machine Learning Models

Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.

scholarly journals Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

2021 ◽  
Author(s):  
Peter Burggraef ◽  
Johannes Wagner ◽  
Benjamin Heinbach ◽  
Fabian Steinberg ◽  
Alejandro Perez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Industry 4.0 ◽  
Predictive Analytics ◽  
Industrial Applications ◽  
Lessons Learned ◽  
Controlled Environment ◽  
Learning Models ◽  
Machine Learning Models

Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.

Application of Class-Based Machine Learning for Potential Hydrocarbon Zones Identification: A Case Study

2021 ◽  
Author(s):  
Sonny Ari Wiyoga ◽  
Jhonny Xu ◽  
Aulia Desiani Carolina ◽  
Ratna Dewanda
Keyword(s):  
Machine Learning ◽  
Limited Resource ◽  
Gaussian Mixture ◽  
Processing Parameters ◽  
Lessons Learned ◽  
Analysis Process ◽  
Analyst Bias ◽  
Crucial Part ◽  
Petrophysical Evaluation

Abstract At times, petrophysicists are expected to evaluate potential of the well in time-constraint situations while maintaining consistency of the parameters and interpretation. Other than that, some challenges may also occur when working with older wells where the dataset are not as complete as current wells and processing parameters are not transferable. In this case study, class-based machine learning (CBML) approach is used to perform petrophysical evaluation to identify potential hydrocarbon zones in the target wells. The objective is to find solution to improve efficiency and consistency in those challenging situations. A class-based machine learning (CBML) workflow uses cross-entropy clustering (CEC)-Gaussian mixture model (GMM)- hidden Markov model (HMM) workflow that identifies locally stationary zones sharing similar statistical properties in logs, and then propagates zonation information from training wells to other wells (Jain, et al., 2019). The workflow is divided into two (2) main steps: training and prediction. Key wells which best represent the formation in the field are used to train the model. This approach automatically generates the number of cluster (class) using unsupervised or supervised depending on the input data. The model from key wells data is then used to reconstruct inputs and outputs along with uncertainty and outlier flags. This allows expert to QC and validate the generated class which is the most crucial part of the workflow. Once the model from the key wells has been built, it is applied to predict the same set of zones in the new wells that require interpretation and predict output curves. The result matched well over the good data interval with the petrophysical interpretation result from conventional approach. While in the bad interval, some discrepancies can be observed. The discrepancy was identified easily from the uncertainty and outlier flags which helps petrophysicists to identify which interval to fix or re-evaluate. Some requirements to condition the input were observed (no missing value over the input and outlier) to get the best result. A number of inputs used in the model need to be consistent over the set of wells used in the training and prediction target. This machine learning workflow speeds-up the petrophysical analysis process, reduce analyst bias and improve consistency result between one well to another within the same field. This machine learning application can also generate auto log QC, zonation class for rock typing also reconstructed logs which enrich the petrophysical interpretation even for wells with limited logs availability. This paper offers practical examples and lessons learned of CBML approach application to perform petrophysical evaluation and identify potential zones while being in time-constrained and limited resource situations.

scholarly journals Predictive analytics in quality assurance for assembly processes: lessons learned from a case study at an industry 4.0 demonstration cell

2021 ◽  
Author(s):  
Peter Burggraef ◽  
Johannes Wagner ◽  
Benjamin Heinbach ◽  
Fabian Steinberg ◽  
Alejandro Perez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Industry 4.0 ◽  
Predictive Analytics ◽  
Industrial Applications ◽  
Lessons Learned ◽  
Controlled Environment ◽  
Learning Models ◽  
Machine Learning Models

Quality assurance (QA) is an important task in manufacturing to assess whether products meet their specifications. However, QA might be expensive, time-consuming, or incomplete. This paper presents a solution for predictive analytics in QA based on machine sensor values during production while employing specialized machine-learning models for classification in a controlled environment. Furthermore, we present lessons learned while implementing this model, which helps to reduce complexity in further industrial applications. The paper’s outcome proves that the developed model was able to predict product quality, as well as to identify the correlation between machine-status and faulty product occurrence.

Faculty Development: A Stage Model Matched to Blended Learning Maturation

2011 ◽  
Vol 15 (1) ◽  
Author(s):  
Michael L. Fetters ◽  
Tova Garcia Duby
Keyword(s):  
Faculty Development ◽  
Blended Learning ◽  
Lessons Learned ◽  
Technology Enhanced Learning ◽  
Stage Model ◽  
Years Of Experience ◽  
Development Programs ◽  
Curriculum Innovation ◽  
Enhanced Learning

Faculty development programs are critical to the implementation and support of curriculum innovation. In this case study, the authors present lessons learned from ten years of experience in faculty development programs created to support innovation in technology enhanced learning. Stages of curriculum innovation are matched to stages of faculty development, and important lessons for success as well as current challenges are delineated and discussed.

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