Recommendations for Data-Driven Degradation Estimation with Case Studies from Manufacturing and Dry-Bulk Shipping

2021 ◽  
pp. 189-204
Author(s):  
Nils Finke ◽  
Marisa Mohr ◽  
Alexander Lontke ◽  
Marwin Züfle ◽  
Samuel Kounev ◽  
...  
Keyword(s):  
Case Studies ◽  
Data Driven ◽  
Bulk Shipping

Data-driven model predictive control for building climate control: Three case studies on different buildings

2019 ◽  
Vol 160 ◽  
pp. 106204 ◽  
Author(s):  
Jiangyu Wang ◽  
Shuai Li ◽  
Huanxin Chen ◽  
Yue Yuan ◽  
Yao Huang
Keyword(s):  
Model Predictive Control ◽  
Case Studies ◽  
Predictive Control ◽  
Data Driven ◽  
Climate Control

scholarly journals Data-driven approaches for modeling train control models: Comparison and case studies

2020 ◽  
Vol 98 ◽  
pp. 349-363 ◽  
Author(s):  
Jiateng Yin ◽  
Shuai Su ◽  
Jing Xun ◽  
Tao Tang ◽  
Ronghui Liu
Keyword(s):  
Case Studies ◽  
Data Driven ◽  
Train Control ◽  
Control Models ◽  
Models Comparison

scholarly journals SPATIAL DATA QUALITY AND A WORKFLOW TOOL

2015 ◽  
Vol XL-3/W3 ◽  
pp. 55-59 ◽  
Author(s):  
M. Meijer ◽  
L. A. E. Vullings ◽  
J. D. Bulens ◽  
F. I. Rip ◽  
M. Boss ◽  
...  
Keyword(s):  
Data Quality ◽  
Case Studies ◽  
Spatial Data ◽  
Selection Criterion ◽  
Data Driven ◽  
Product Characteristics ◽  
Spatial Data Quality ◽  
Quality Specifications ◽  
Product Specifications

Although by many perceived as important, spatial data quality has hardly ever been taken centre stage unless something went wrong due to bad quality. However, we think this is going to change soon. We are more and more relying on data driven processes and due to the increased availability of data, there is a choice in what data to use. How to make that choice? We think spatial data quality has potential as a selection criterion. <br><br> In this paper we focus on how a workflow tool can help the consumer as well as the producer to get a better understanding about which product characteristics are important. For this purpose, we have developed a framework in which we define different roles (consumer, producer and intermediary) and differentiate between product specifications and quality specifications. A number of requirements is stated that can be translated into quality elements. We used case studies to validate our framework. This framework is designed following the fitness for use principle. Also part of this framework is software that in some cases can help ascertain the quality of datasets.

scholarly journals Transferrable Selectivity Profiles Enable Prediction in Synergistic Catalyst Space

2021 ◽  
Author(s):  
Yutao Kuang ◽  
Jolene Reid
Keyword(s):  
Structure Function ◽  
Case Studies ◽  
Asymmetric Catalysis ◽  
Data Driven ◽  
Quantitative Structure ◽  
Chiral Molecule ◽  
Screening Process ◽  
Chiral Catalyst ◽  
Reaction Systems ◽  
Significant Barrier

Organometallic intermediates participate in many multi-catalytic enantioselective transformations directed by a chiral catalyst, but the requirement of optimizing two catalyst components is a significant barrier to widely adopting this approach for chiral molecule synthesis. Algorithms can potentially accelerate the screening process by developing quantitative structure-function relationships from large experimental datasets. However, the chemical data available in this catalyst space is limited. We report a data-driven strategy that effectively translates selectivity relationships trained on enantioselectivity outcomes derived from one catalyst reaction systems where an abundance of data exists, to synergistic catalyst space. We describe three case studies involving different modes of catalysis (Brønsted acid, chiral anion, and secondary amine) that substantiate the prospect of this approach to predict and elucidate selectivity in reactions where more than one catalyst is involved. Ultimately, the success in applying our approach to diverse areas of asymmetric catalysis implies that this general workflow should find broad use in the study and development of new enantioselective, multi-catalytic processes.

scholarly journals Explaining the competitive advantage generated from Analytics with the knowledge-based view: the example of Logistics and Supply Chain Management

2019 ◽  
Vol 13 (1) ◽  
pp. 163-214 ◽  
Author(s):  
Tino T. Herden
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Competitive Advantage ◽  
Case Studies ◽  
Data Driven ◽  
Sustainable Competitive Advantage ◽  
Chain Management ◽  
Knowledge Based ◽  
Data Scientist ◽  
Functional Teams

AbstractThe purpose of this paper is to provide a theory-based explanation for the generation of competitive advantage from Analytics and to examine this explanation with evidence from confirmatory case studies. A theoretical argumentation for achieving sustainable competitive advantage from knowledge unfolding in the knowledge-based view forms the foundation for this explanation. Literature about the process of Analytics initiatives, surrounding factors, and conditions, and benefits from Analytics are mapped onto the knowledge-based view to derive propositions. Eight confirmatory case studies of organizations mature in Analytics were collected, focused on Logistics and Supply Chain Management. A theoretical framework explaining the creation of competitive advantage from Analytics is derived and presented with an extensive description and rationale. This highlights various aspects outside of the analytical methods contributing to impactful and successful Analytics initiatives. Thereby, the relevance of a problem focus and iterative solving of the problem, especially with incorporation of user feedback, is justified and compared to other approaches. Regarding expertise, the advantage of cross-functional teams over data scientist centric initiatives is discussed, as well as modes and reasons of incorporating external expertise. Regarding the deployment of Analytics solutions, the importance of consumability, users assuming responsibility of incorporating solutions into their processes, and an innovation promoting culture (as opposed to a data-driven culture) are described and rationalized. Further, this study presents a practical manifestation of the knowledge-based view.

Utilizing Program Specific Data to Develop Case Studies for Use With Preservice Teachers

2017 ◽  
pp. 1-20
Author(s):  
Heather M. Reynolds ◽  
A. Tina Wagle
Keyword(s):  
Preservice Teachers ◽  
Case Studies ◽  
Higher Order Thinking ◽  
Data Driven ◽  
Teacher Education Programs ◽  
Education Programs ◽  
Specific Data ◽  
Graduate Coursework ◽  
Relevant Case ◽  
Modeling Data

With a focus on data driven decision making, teacher education programs need to prepare preservice teachers to analyze data while modeling data driven practices in our own programs. Research has demonstrated the effectiveness of using case studies to promote critical thinking, analysis and interpretation, and higher order thinking. This study utilized the results from surveys of residents enrolled in a clinically rich residency program to develop and implement relevant case studies for use in program coursework. The utility of using case studies in graduate coursework was evaluated through a survey of current residents. The theoretical and practical value of creating case studies based on program specific challenges, and examples of the case studies that were generated from this data will be shared.

The identification of stages in diachronic data: variability-based neighbour clustering

Corpora ◽  
2008 ◽  
Vol 3 (1) ◽  
pp. 59-81 ◽  
Author(s):  
Stefan Th. Gries ◽  
Martin Hilpert
Keyword(s):  
Case Studies ◽  
Hierarchical Clustering ◽  
The Other ◽  
Data Driven ◽  
Clustering Method ◽  
Chronological Order ◽  
Bottom Up ◽  
Data Variability ◽  
Data Points ◽  
Corpus Data

In this paper, we introduce a data-driven bottom-up clustering method for the identification of stages in diachronic corpus data that differ from each other quantitatively. Much like regular approaches to hierarchical clustering, it is based on identifying and merging the most cohesive groups of data points, but, unlike regular approaches to clustering, it allows for the merging of temporally adjacent data, thus, in effect, preserving the chronological order. We exemplify the method with two case studies, one on verbal complementation of shall, the other on the development of the perfect in English.

scholarly journals Supporting M5 model trees with sensitivity information derived from conceptual hydrological models

2015 ◽  
Vol 17 (6) ◽  
pp. 943-958 ◽  
Author(s):  
Carolina Massmann
Keyword(s):  
Time Series ◽  
Case Studies ◽  
Parameter Sensitivity ◽  
Data Driven ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Model Trees ◽  
Sensitivity Indices ◽  
M5 Model ◽  
Process Based Models

The main objective of this paper is assessing the usefulness of parameter sensitivity information from conceptual hydrological models for data-driven models, an approach which might allow us to take advantage of the strengths of both data-based and process-based models. This study uses the parameter sensitivity of three widely used conceptual hydrological models (GR4J, Hymod and SAC-SMA) and combines them with M5 model trees. The study was carried out for three case studies dealing with different problems to which model trees are applied: one using model trees as error correctors and two case studies in which model trees were used as rainfall–runoff models and which differ in how the sensitivity information is used. The results show that sensitivity time series can improve the predictions of M5 model trees, especially when they do not include the time series of previous discharge as predictor variables. The use of parameter sensitivity information for clustering the time series resulted in model trees that had a structure consistent with the hydrological processes that were taking place in the considered cluster, indicating that the use of sensitivity indices could be a viable way of introducing hydrological knowledge into data-based models.

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