@misc{9206,
  abstract     = {{Failure in time (FIT) is an important measure for the reliability of electrical connectors. Due to the very long lifetime of connectors, the tests for the determination of FIT rate are time and labour intensive. In this paper a data driven method using a statistical process to estimate the FIT rate of electrical connectors with data of electrical contact resistance development in short term tests is proposed. The results of prediction are then compared with the results from long term tests. The study shows a strong correlation between contact resistance development in short term tests and the development of the number of failures in later stages of tests. In order to predict the development of degradation precisely, the distribution of resistance data in many different tests with different connectors is investigated. The Generalized Extreme Value Distribution, which reveals an ideal fitting, has been implemented for the prediction of the failure rates of connectors, thereby enabling a remarkable time-lapse of lifetime tests. This method can also be employed in the prognosis and management of system health through the forecast of health of connectors in different systems in operation.}},
  author       = {{Song, Jian and Shukla, Abhay Rammurti and Probst, Roman}},
  booktitle    = {{Microelectronics reliability : an internat. journal & world abstracting service}},
  issn         = {{0026-2714}},
  keywords     = {{Electrical connectors, Prediction of lifetime, FIT, Correlation between data in short and long term tests, Time-lapse of lifetime tests}},
  number       = {{November 2022}},
  publisher    = {{Elsevier}},
  title        = {{{Prediction of failure in time (FIT) of electrical connectors with short term tests}}},
  doi          = {{10.1016/j.microrel.2022.114684}},
  volume       = {{138}},
  year         = {{2022}},
}

@article{6839,
  abstract     = {{Pasteurization is a crucial processing method in the food industry to ensure the safety of consumables. A major part of contemporary pasteurization processes involves using flash pasteurizer systems, where liquids are pumped through a pipe system to heat them for a predefined time. Accurately monitoring the amount of heat treatment applied to a product is challenging. This monitoring helps ensure that the correct heat impact (expressed in pasteurization units) is applied, which is commonly calculated as a product of time and temperature, taking achievability of the inactivation of the microorganisms into account. The state-of-the-art method involves a calculation of the applied pasteurization units using a one-point temperature measurement and the holding time for this temperature. Concerns about accuracy lead to high safety margins, reducing the quality of the pasteurized product. In this study, the applied pasteurization level was estimated using regression models trained with NIR spectroscopy data collected while pasteurizing fruit juices of different types and brands. Several conventional regression models were trained in combination with different preprocessing methods, including a novel prediction outlier detection method. Generalized juice models trained with the concatenated data of all types of juices demonstrated cross-validated scores of RMSECV ∼2.78 ± 0.09 and r<jats:sup>2</jats:sup> 0.96 ± 0.01, while separate juice models displayed averaged cross-validated scores of RMSECV ∼1.56 ± 0.04 and r<jats:sup>2</jats:sup> 0.98 ± 0.01. Thus, the model accuracy ±10–30 % is well within the standard safety margins. }},
  author       = {{Sürmeli, Baris Gün and Weishaupt, Imke and Schwarzer, Knut and Moriz, Natalia and Schneider, Jan}},
  issn         = {{1751-6552}},
  journal      = {{Journal of Near Infrared Spectroscopy}},
  keywords     = {{Beverage pasteurization, heat impact control, prediction outlier elimination}},
  number       = {{6}},
  pages        = {{339--351}},
  publisher    = {{Sage Publishing}},
  title        = {{{Heat impact control in flash pasteurization by estimation of applied pasteurization units using near infrared spectroscopy}}},
  doi          = {{10.1177/09670335211057233}},
  volume       = {{29}},
  year         = {{2021}},
}

@misc{12786,
  abstract     = {{One goal in Bayesian machine learning is to encode prior knowledge into prior distributions, to model data efficiently. We consider prior knowledge from systems of linear partial differential equations together with their boundary conditions. We construct multi-output Gaussian process priors with realizations in the solution set of such systems, in particular only such solutions can be represented by Gaussian process regression. The construction is fully algorithmic via Grobner bases and it does not employ any approximation. It builds these priors combining two parametrizations via a pullback: the first parametrizes the solutions for the system of differential equations and the second parametrizes all functions adhering to the boundary conditions.}},
  author       = {{Lange-Hegermann, Markus}},
  booktitle    = {{24th International Conference on Artificial Intelligence and Statistics (AISTATS)}},
  editor       = {{Banerjee, A. and Fukumizu, K.}},
  issn         = {{2640-3498}},
  keywords     = {{FUNCTIONAL REGRESSION, PREDICTION, ALGORITHMS, COMPLEXITY, MODELS}},
  location     = {{Virtual}},
  publisher    = {{MLResearchPress }},
  title        = {{{Linearly Constrained Gaussian Processes with Boundary Conditions}}},
  volume       = {{130}},
  year         = {{2021}},
}

@article{2183,
  abstract     = {{To connect terminals in a cyber–physical system, large quantities of electrical contacts are used. In order to guarantee a high reliability of the system, the lifetime of the electrical contacts should be very long. Thus, it is of great importance to understand the failure mechanism and then to predict the lifetime of the electrical contacts. For the applications under high thermal and/or mechanical loads, noble plating is a good choice, considering its inertness to oxidation. For noble plating, one of the most critical failure mechanisms is the fretting wear. Wear debris generated in the contact area, acting as the third bodies, will greatly influence the further wear behavior and electrical performance. In this study, the state of the art regarding third bodies is firstly reviewed, and then the influence of the third bodies on the wear and electrical performance is investigated, from the aspects of lifetime and the element distributions in contact area. Finally, an example of prediction of the wear of noble plating is shown with the consideration of the third bodies. Based on this study, by involving the third bodies, the wear of noble plating can be predicted with a higher accuracy.}},
  author       = {{Yuan, Haomiao and Song, Jian}},
  issn         = {{2227-7080}},
  journal      = {{Technologies}},
  keywords     = {{electrical contacts, noble plating, third bodies, wear prediction}},
  number       = {{4}},
  title        = {{{An Improved Calculation Model for the Prediction of the Wear of Coated Electrical Contacts}}},
  doi          = {{10.3390/technologies7040077}},
  volume       = {{7}},
  year         = {{2019}},
}

@inproceedings{594,
  abstract     = {{Due to steadily increased demand for customized products, as well as their enhanced complexity and shorter product lifecycles, companies in all industries require a reliable prediction of the expected product development costs from the very start of product realization. Incorrectly estimated project costs may lead to serious consequences in the course of a development project. For example, offers are most often based on such early cost estimations and consequently, a major safety margin has to be added, which may result in the refusal of an order. A too low estimation of the costs of aproduct development project, on the other hand, may result in a loss for the project.In this paper, a software tool is presented for the prediction of product development costs which offers the user the ability to create a more accurate prediction of project costs on the basis of a minimum of retrograde project information. By combining a parametric cost model and cost result with stochastic character, based on the Monte Carlo method, in one software system, it is possible to significantly improve projectcost estimations.}},
  author       = {{Otte, Andreas and Scheideler, Eva and Villmer, Franz-Josef}},
  booktitle    = {{Department of Production Engineering and Management}},
  editor       = {{Villmer, Franz-Josef and Padoano, Elio}},
  isbn         = {{978-3-946856-00-9}},
  keywords     = {{Cost prediction, Product realization projects, Monte Carlo method, Parametric cost model, Software tool}},
  location     = {{Lemgo}},
  number       = {{1}},
  pages        = {{281--292}},
  title        = {{{Project Cost Estimator - A Parameter-Based Tool to Predict Product Realization Costs at a Very Early Stage}}},
  year         = {{2016}},
}

@inbook{2394,
  abstract     = {{For the production of biopharmaceuticals a seed train is required to generate an adequate number of cells for inoculation of the production bioreactor. This seed train is time- and cost-intensive but offers potential for optimization. A method and a protocol are described for the seed train mapping, directed modeling without major effort, and its optimization regarding selected optimization criteria such as optimal points in time for cell passaging. Furthermore, the method can also be applied for the set-up of a new seed train, for example for a new cell line. Although the chapter is directed towards suspension cell lines, the method is also generally applicable, e.g. for adherent cell lines.}},
  author       = {{Frahm, Björn}},
  booktitle    = {{Animal Cell Biotechnology}},
  isbn         = {{9781627037327}},
  issn         = {{1064-3745}},
  keywords     = {{Seed train Optimization Modeling Prediction Space-Time-Yield (STY) Systems approach Bioinformatics Computational biotechnology Suspension Production}},
  pages        = {{355--367}},
  publisher    = {{Humana Press}},
  title        = {{{Seed Train Optimization for Cell Culture}}},
  doi          = {{10.1007/978-1-62703-733-4_22}},
  volume       = {{1104}},
  year         = {{2013}},
}

@inbook{10214,
  abstract     = {{For the production of biopharmaceuticals a seed train is required to generate an adequate number of cells for inoculation of the production bioreactor. This seed train is time- and cost-intensive but offers potential for optimization. A method and a protocol are described for the seed train mapping, directed modeling without major effort, and its optimization regarding selected optimization criteria such as optimal points in time for cell passaging. Furthermore, the method can also be applied for the set-up of a new seed train, for example for a new cell line. Although the chapter is directed towards suspension cell lines, the method is also generally applicable, e.g. for adherent cell lines.}},
  author       = {{Frahm, Björn}},
  booktitle    = {{Animal Cell Biotechnology - Methods and Protocols}},
  editor       = {{Pörtner, Ralf}},
  isbn         = {{978-1-62703-732-7}},
  issn         = {{1940-6029}},
  keywords     = {{Seed train, Optimization, Modeling, Prediction, Space-Time-Yield (STY), Systems approach, Bioinformatics, Computational biotechnology, Suspension, Production}},
  pages        = {{355–367}},
  publisher    = {{Humana Press}},
  title        = {{{Seed Train Optimization for Cell Culture}}},
  doi          = {{10.1007/978-1-62703-733-4_22}},
  volume       = {{1104}},
  year         = {{2013}},
}