Architecture for Enabling Edge Inference via Model Transfer from Cloud Domain in a Kubernetes Environment

The current approaches for energy consumption optimisation in buildings are mainly reactive or focus on scheduling of daily/weekly operation modes in heating. Machine Learning (ML)-based advanced control methods have been demonstrated to improve energy efficiency when compared to these traditional methods. However, placing of ML-based models close to the buildings is not straightforward. Firstly, edge-devices typically have lower capabilities in terms of processing power, memory, and storage, which may limit execution of ML-based inference at the edge. Secondly, associated building information should be kept private. Thirdly, network access may be limited for serving a large number of edge devices. The contribution of this paper is an architecture, which enables training of ML-based models for energy consumption prediction in private cloud domain, and transfer of the models to edge nodes for prediction in Kubernetes environment. Additionally, predictors at the edge nodes can be automatically updated without interrupting operation. Performance results with sensor-based devices (Raspberry Pi 4 and Jetson Nano) indicated that a satisfactory prediction latency (~7–9 s) can be achieved within the research context. However, model switching led to an increase in prediction latency (~9–13 s). Partial evaluation of a Reference Architecture for edge computing systems, which was used as a starting point for architecture design, may be considered as an additional contribution of the paper

Simulation environment for the C14-experiment

In this thesis, a simulation environment utilizing Geant4 simulation toolkit was constructed to study the C14 experiment located at the Pyhäsalmi Mine, Finland. The C14 experiment aims to find a liquid scintillator sample with the least amount of 14C, the radioactive isotope of carbon, with concentration preferably less than 10^-18. The scintillator detector used in the experiment consists of a 1.6 liter cylindrical vessel filled with liquid scintillator, two 20 cm conical light guides connected to the ends of the vessel and two low-background photomultiplier tubes to detect scintillator light. In addition, the detector is covered with a reflecting foil. The simulation environment was used to study the energy resolution and intrinsic background of the detector. The energy resolution of the experiment was simulated for electrons and alpha particles of energies 0-2 MeV (electron) and 3-10 MeV (alpha particle), which well cover the b-decay energy range of 14C. In the absence foil, the energy spectrum was highly position-dependent as particles originating near the edges of the vessel yielded more light than particles in the center. As a result, the energy resolution was worse without the foil. The most prominent source of intrinsic background was gamma rays from the photomultiplier tubes. The liquid bulk consisting of linear alkyl benzene did not cause a noticeable background unless the 222Rn concentration was of the order of 1 Bq/m3 but even still the gamma rays were dominant

Cross-Kerr nonlinearity : a stability analysis

We analyse the combined e ect of the radiation-pressure and cross-Kerr nonlinearity on the stationary solution of the dynamics of a nanomechanical resonator interacting with an electromagnetic cavity. Within this setup, we show how the optical bistability picture induced by the radiation-pressure force is modi ed by the presence of the cross-Kerr interaction term. More speci cally, we show how the optically bistable region, characterising the pure radiation-pressure case, is reduced by the presence of a cross-Kerr coupling term. At the same time, the upper unstable branch is extended by the presence of a moderate cross-Kerr term, while it is reduced for larger values of the cross-Kerr coupling.peerReviewe