41,270 research outputs found
JoGebert/PowerMan-Online-Power-Capping-by-ML: Initial Release
No full textInitial release for reproducing the results of the Research Poster "PowerMan: Online Power Capping by Computationally Informed Machine Learning" submitted for SC22 on August 5, 2022
Machine learning and digital twins: monitoring and control for dynamic security in power systems
No full textThe reader of the chapter will be able to connect techniques from machine learning (ML) and digital twins (DTs) to gain insights for monitoring and control of (dynamic) security for electrical power systems. DTs are validated and verified high-fidelity (hf) models providing high simulation accuracy. DTs can be used for simulation of the supervised process of system operation and are therefore able to provide synthetic studied data, where measurement data are scarce. However, for some real-time applications in monitoring and control, such high-fidelity simulation models are not appropriate due to the corresponding computational barrier. There, ML aims to create an application-specific, low-fidelity (lf) approximation of the digital twin. Such trained lf models are used in real-time applications where computational time is scarce and lf information is sufficient. The conceptual intersection of hf and lf models has been little explored and becomes increasingly complex. This chapter aims to provide a conceptual overview of how such hf and lf models can be combined. This chapter is split into two parts where the first part is to introduce ML, lf models, and digital twins, hf models, for power systems analysis, and the second chapter is to use these two types of models to form purpose-driven surrogate lf models, illustrated on the example of dynamic security assessment (DSA). In the first part, the concepts for using DTs as hf models for online power system studies and their corresponding tuning of model parameters are introduced. Subsequently, ML i.e., lf models, are introduced and their corresponding training frameworks. Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Intelligent Electrical Power Grid
Power-Law equations of the material parameters in continuous and discontinuous approaches (BMD [gr/ml]).
No full textPower-Law equations of the material parameters in continuous and discontinuous approaches (BMD [gr/ml]).</p
Metadata Representations for Queryable ML Model Zoos
No full textMachine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that are useful for reporting, auditing, reproducibility, and interpretability purposes. The metatada is currently not standardised; its expressivity is limited; and there is no interoperable way to store and query it. Consequently, model search, reuse, comparison, and composition are hindered. In this paper, we advocate for standardized ML model metadata representation and management, proposing a toolkit supported to help practitioners manage and query that metadata.Web Information SystemsHuman-Centred Artificial Intelligenc
A Manifesto of Nodalism
Get PDFThis paper proposes the notion of Nodalism as a means describing contemporary culture and of understanding my own creative practice in electronic music composition. It draws on theories and ideas from Kirby, Bauman, Bourriaud, Deleuze, Guatarri, and Gochenour, to demonstrate how networks of ideas or connectionist neural models of cognitive behaviour can be used to contextualize, understand and become a creative tool for the creation of contemporary electronic music
Optimizing ML Inference Queries Under Constraints
No full textThe proliferation of pre-trained ML models in public Web-based model zoos facilitates the engineering of ML pipelines to address complex inference queries over datasets and streams of unstructured content. Constructing optimal plan for a query is hard, especially when constraints (e.g. accuracy or execution time) must be taken into consideration, and the complexity of the inference query increases. To address this issue, we propose a method for optimizing ML inference queries that selects the most suitable ML models to use, as well as the order in which those models are executed. We formally define the constraint-based ML inference query optimization problem, formulate it as a Mixed Integer Programming (MIP) problem, and develop an optimizer that maximizes accuracy given constraints. This optimizer is capable of navigating a large search space to identify optimal query plans on various model zoos.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information SystemsHuman-Centred Artificial Intelligenc
A robust data-driven method for open-circuit fault diagnosis of power switches in three-phase inverters with low-quality data
No full textMachine learning (ML) techniques have shown great potential for power converter fault diagnosis. However, the data measured by the diagnostic processor may be corrupted in real-world applications, which would degrade the performance of ML-based diagnostic models. This article proposed a robust data-driven method for power switch open-circuit fault diagnosis under low-quality data issues with missing values, outliers, noises. At offline stage, a robust subspace matrix is first trained and adopted to recover the corrupted data from missing data and outliers. Then, the recovered data is further denoized through joint sparse coding and transform learning, where a transform weight matrix can be obtained. By using the processed data as the input, a random vector functional link network is trained to generate the diagnostic model. At online stage, real-time current signals are firstly recovered\denoized based on the trained matrixes and then sent to the trained diagnostic model to generate the diagnostic result. Simulation and real-time tests have demonstrated the high accuracy and strong robustness of the proposed method under various scenarios.Ministry of Education (MOE)This work was supported by the Ministry of Education, Republic of Singapore, under Grant AcRF TIER-1 RT9/22
125 ml glass bottle
No full textStandard glass bottle for labware. Modeled after Wheaton 125 ml bottle
Into the Wild: Dissemination of Antibiotic Resistance Determinants via a Species Recovery Program
Get PDFManagement strategies associated with captive breeding of endangered species can establish opportunities for transfer of pathogens and genetic elements between human and animal microbiomes. The class 1 integron is a mobile genetic element associated with clinical antibiotic resistance in gram-negative bacteria. We examined the gut microbiota of endangered brush-tail rock wallabies Petrogale penicillata to determine if they carried class 1 integrons. No integrons were detected in 65 animals from five wild populations. In contrast, class 1 integrons were detected in 48% of fecal samples from captive wallabies. The integrons contained diverse cassette arrays that encoded resistance to streptomycin, spectinomycin, and trimethoprim. Evidence suggested that captive wallabies had acquired typical class 1 integrons on a number of independent occasions, and had done so in the absence of strong selection afforded by antibiotic therapy. Sufficient numbers of bacteria containing diverse class 1 integrons must have been present in the general environment occupied by the wallabies to account for this acquisition. The captive wallabies have now been released, in an attempt to bolster wild populations of the species. Consequently, they can potentially spread resistance integrons into wild wallabies and into new environments. This finding highlights the potential for genes and pathogens from human sources to be acquired during captive breeding and to be unwittingly spread to other populations
Analysis of a Multilevel Dual Active Bridge (ML-DAB) DC-DC Converter Using Symmetric Modulation
No full textDual active bridge (DAB) converters have been popular in high voltage, low and medium power DC-DC applications, as well as an intermediate high frequency link in solid state transformers. In this paper, a multilevel DAB (ML-DAB) has been proposed in which two active bridges produce two-level (2L)-5L, 5L-2L and 3L-5L voltage waveforms across the high frequency transformer. The proposed ML-DAB has the advantage of being used in high step-up/down converters, which deal with higher voltages, as compared to conventional two-level DABs. A three-level neutral point diode clamped (NPC) topology has been used in the high voltage bridge, which enables the semiconductor switches to be operated within a higher voltage range without the need for cascaded bridges or multiple two-level DAB converters. A symmetric modulation scheme, based on the least number of angular parameters rather than the duty-ratio, has been proposed for a different combination of bridge voltages. This ML-DAB is also suitable for maximum power point tracking (MPPT) control in photovoltaic applications. Steady-state analysis of the converter with symmetric phase-shift modulation is presented and verified using simulation and hardware experiments
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