Data-Driven Capacity Management with Machine Learning: A Novel Approach and a Case-Study for a Public Service Office

2018 ◽  
pp. 105-115 ◽  
Author(s):  
Fabian Taigel ◽  
Jan Meller ◽  
Alexander Rothkopf
Keyword(s):  
Machine Learning ◽  
Public Service ◽  
Capacity Management ◽  
Data Driven ◽  
Novel Approach

scholarly journals Smart mobility, age and data justice

2020 ◽  
pp. 146144482090268 ◽  
Author(s):  
Maria Sourbati ◽  
Frauke Behrendt
Keyword(s):  
Public Service ◽  
Social Inclusion ◽  
Data Gathering ◽  
Data Driven ◽  
Policy Makers ◽  
Mobility Data ◽  
Strategic Approach ◽  
Data Gaps ◽  
Smart Mobility

This article examines converging trends in ageing, digitalisation and datafication in the context of mobility and transport. While mobility data are increasingly captured by (public) transport and mobility as a service (MaaS) providers, Internet of Things (IoT) vehicles, apps and so on, the increasing entanglement of mobility and datafication happens unevenly, for example, in relation to age. This is particularly significant in the light of the rise of data-driven policy-making, and its potential impacts on mobility provision for older people. The article highlights new questions for public policy around data gaps and social inclusion and examines them through a UK case study. The results show that old age and mobility is an area with significant gaps in the data available to policy makers. A key recommendation is for commissioning bodies to develop a strategic approach to structured data gathering and analysis that addresses issues of exclusion from smart public service infrastructure.

scholarly journals Reaction Wheels Fault Isolation Onboard 3-Axis Controlled Satel-lite using Enhanced Random Forest with Multidomain Features

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Afshin Rahimi ◽  
Mofiyinoluwa O. Folami
Keyword(s):  
Machine Learning ◽  
Fault Isolation ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Alternative Methods ◽  
Model Parameters ◽  
High Fidelity ◽  
Novel Approach ◽  
Learning Techniques

As the number of satellite launches increases each year, it is only natural that an interest in the safety and monitoring of these systems would increase as well. However, as a system becomes more complex, generating a high-fidelity model that accurately describes the system becomes complicated. Therefore, imploring a data-driven method can provide to be more beneficial for such applications. This research proposes a novel approach for data-driven machine learning techniques on the detection and isolation of nonlinear systems, with a case-study for an in-orbit closed loop-controlled satellite with reaction wheels as actuators. High-fidelity models of the 3-axis controlled satellite are employed to generate data for both nominal and faulty conditions of the reaction wheels. The generated simulation data is used as input for the isolation method, after which the data is pre-processed through feature extraction from a temporal, statistical, and spectral domain. The pre-processed features are then fed into various machine learning classifiers. Isolation results are validated with cross-validation, and model parameters are tuned using hyperparameter optimization. To validate the robustness of the proposed method, it is tested on three characterized datasets and three reaction wheel configurations, including standard four-wheel, three-orthogonal, and pyramid. The results prove superior performance isolation accuracy for the system under study compared to previous studies using alternative methods (Rahimi & Saadat, 2019, 2020).

scholarly journals Model-based tumor subclonal reconstruction

2019 ◽  
Author(s):  
Giulio Caravagna ◽  
Timon Heide ◽  
Marc Williams ◽  
Luis Zapata ◽  
Daniel Nichol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evolutionary Theory ◽  
Data Driven ◽  
Next Generation Sequencing Data ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Model Based ◽  
Novel Approach ◽  
History Of ◽  
Multiple Samples

AbstractThe vast majority of cancer next-generation sequencing data consist of bulk samples composed of mixtures of cancer and normal cells. To study tumor evolution, subclonal reconstruction approaches based on machine learning are used to separate subpopulation of cancer cells and reconstruct their ancestral relationships. However, current approaches are entirely data-driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in subclonal reconstruction if tumor evolution is not accounted for, and that those errors increase when multiple samples are taken from the same tumor. To address this issue, we present a novel approach for model-based subclonal reconstruction that combines data-driven machine learning with evolutionary theory. Using public, synthetic and newly generated data, we show the method is more robust and accurate than current techniques in both single-sample and multi-region sequencing data. With careful data curation and interpretation, we show how the method allows minimizing the confounding factors that affect non-evolutionary methods, leading to a more accurate recovery of the evolutionary history of human tumors.

scholarly journals Data-Driven Machine Learning System for Optimization of Processes Supporting the Distribution of Goods and Services – a case study

2020 ◽  
Vol 44 ◽  
pp. 60-67
Author(s):  
Zbigniew Tarapata ◽  
Tadeusz Nowicki ◽  
Ryszard Antkiewicz ◽  
Jaroslaw Dudzinski ◽  
Konrad Janik
Keyword(s):  
Machine Learning ◽  
Learning System ◽  
Data Driven ◽  
Goods And Services

scholarly journals Enhancing Failure Mode and Effects Analysis Using Auto Machine Learning: A Case Study of the Agricultural Machinery Industry

Processes ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 224 ◽  
Author(s):  
Sami Sader ◽  
István Husti ◽  
Miklós Daróczi
Keyword(s):  
Machine Learning ◽  
Failure Mode ◽  
High Accuracy ◽  
Risk Priority Number ◽  
Learning Models ◽  
Average Value ◽  
Novel Approach ◽  
One Year ◽  
Machine Learning Models

In this paper, multiclass classification is used to develop a novel approach to enhance failure mode and effects analysis and the generation of risk priority number. This is done by developing four machine learning models using auto machine learning. Failure mode and effects analysis is a technique that is used in industry to identify possible failures that may occur and the effects of these failures on the system. Meanwhile, risk priority number is a numeric value that is calculated by multiplying three associated parameters namely severity, occurrence and detectability. The value of risk priority number determines the next actions to be made. A dataset that includes a one-year registry of 1532 failures with their description, severity, occurrence, and detectability is used to develop four models to predict the values of severity, occurrence, and detectability. Meanwhile, the resulted models are evaluated using 10% of the dataset. Evaluation results show that the proposed models have high accuracy whereas the average value of precision, recall, and F1 score are in the range of 86.6–93.2%, 67.9–87.9%, 0.892–0.765% respectively. The proposed work helps in carrying out failure mode and effects analysis in a more efficient way as compared to the conventional techniques.

scholarly journals Capacity management in public service facility networks: a model, computational tests and a case study

2015 ◽  
Vol 10 (5) ◽  
pp. 975-995 ◽  
Author(s):  
Giuseppe Bruno ◽  
Andrea Genovese ◽  
Carmela Piccolo
Keyword(s):  
Public Service ◽  
Capacity Management ◽  
Service Facility
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